<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Toppin, K.</style></author><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design Weight and Calibration</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">Dual System Estimation; Weights; Census of Agriculture</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Toppin_CaRC_20170926.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The USDA’s National Agricultural Statistics Service (NASS) conducts the U.S. Census of Agriculture in years ending in 2 and 7. Population estimates from the census are adjusted for undercoverage,&amp;nbsp;non-response and misclassification and calibrated to known population totals. These&amp;nbsp;adjustments are reflected in weights that are attached to each responding unit. Calculating these&amp;nbsp;weights has been a two-part procedure. First, one calculates initial (Dual System Estimation or&amp;nbsp;DSE) weights that account for under-coverage, non-response and misclassification. and in the second&amp;nbsp;step, calibration is used to adjust the weights by forcing the weighted estimates obtained in the&amp;nbsp;first step to match known population totals. Recently, a calibration algorithm, Integer Calibration&lt;br /&gt;
(INCA), was developed to produce integer calibrated weights as required in NASS publications.&amp;nbsp;This paper considers combining the two steps of calculating weights into one. This new algorithm&amp;nbsp;is based on a regularized constrained dual system estimation methodology, which combines&amp;nbsp;capture-recapture and calibration (CaRC).&lt;/p&gt;
</style></abstract><custom1><style face="normal" font="default" size="100%">&lt;p&gt;Download:&amp;nbsp;https://www.niss.org/sites/default/files/Toppin_CaRC_20170926.pdf&lt;/p&gt;
</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Toppin, K.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimated Covariance Matrices Associated with Calibration</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">Calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">Census</style></keyword><keyword><style  face="normal" font="default" size="100%">Estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword><keyword><style  face="normal" font="default" size="100%">Survey</style></keyword><keyword><style  face="normal" font="default" size="100%">Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighting</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Sartore_Variance_Estim_20170926.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Surveys often provide numerous estimates of population parameters. Some of the population values&amp;nbsp;may be known to lie within a small range of values with a high level of certainty. Calibration is used&amp;nbsp;to adjust survey weights associated with the observations within a data set. This process ensures&amp;nbsp;that the “sample” estimates for the target population totals (benchmarks) lie within the anticipated&amp;nbsp;ranges of those population values. The additional uncertainty due to the calibration process needs&amp;nbsp;to be captured. In this paper, some methods for estimating the variance of the population totals are&amp;nbsp;proposed for an algorithmic calibration process based on minimizing the L1-norm relative error.&amp;nbsp;The estimated covariance matrices for the calibration totals are produced either by linear approximations&amp;nbsp;or bootstrap techniques. Specific data structures are required to allow for the computation&amp;nbsp;of massively large covariance matrices. In particular, the implementation of the proposed algorithms&amp;nbsp;exploits sparse matrices to reduce the computational burden and memory usage. The computational&amp;nbsp;efficiency is shown by a simulation study.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abernethy, J.</style></author><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Benecha, H.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of Capture Probabilities by Accounting for Sample Designs</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">CaptureRecapture</style></keyword><keyword><style  face="normal" font="default" size="100%">Estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">government</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword><keyword><style  face="normal" font="default" size="100%">Research</style></keyword><keyword><style  face="normal" font="default" size="100%">SampleDesigns</style></keyword><keyword><style  face="normal" font="default" size="100%">Weights</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Abernethy_Capture_Probs_20170920.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducts the Census of Agriculture every five years to estimate the number of U.S. farms, as well as other agriculturally related population totals. NASS applies a Dual-System Estimation (DSE) methodology on data collected from the Census and the June Area Survey (JAS) to estimate the number of farms in the U.S.. Traditional multinomial-based capture-recapture methodology requires a model to estimate the probability of capture for every captured operation on either survey. Of course, the selection probabilities associated with the JAS area frame design are different from those associated with the Census. Such a difference makes it difficult to compute the exact JAS selection probabilities for farm records captured only by the Census. For this reason, we propose and compare three methods for estimating the overall capture probability. The first two methods involve approximating the JAS selection probabilities and the third conditions them out. We compare these three techniques to investigate their precision through a simulation study.&lt;/p&gt;
</style></abstract><custom1><style face="normal" font="default" size="100%">&lt;p&gt;In Proceedings of the Government Statistics Section, JSM 2017. Download&amp;nbsp;https://www.niss.org/sites/default/files/Abernethy_Capture_Probs_20170920.pdf&lt;/p&gt;
</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benecha, H.</style></author><author><style face="normal" font="default" size="100%">Abreu, D.</style></author><author><style face="normal" font="default" size="100%">Abernethy, J.</style></author><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Young, L. Y.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%"> Evaluation of a New Approach for Estimating the Number of U.S. Farms</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">Area-frame</style></keyword><keyword><style  face="normal" font="default" size="100%">BigData</style></keyword><keyword><style  face="normal" font="default" size="100%">Capture-Recapture</style></keyword><keyword><style  face="normal" font="default" size="100%">List Frame</style></keyword><keyword><style  face="normal" font="default" size="100%">Logistic Regression</style></keyword><keyword><style  face="normal" font="default" size="100%">Misclassification Error</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Benecha_Estim_Farms_20170929.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;USDA’s National Agricultural Statistics Service (NASS) employs the June Area Survey (JAS) to produce annual&amp;nbsp;estimates of U.S. farm numbers. The JAS is an area-frame-based survey conducted every year during the first two&amp;nbsp;weeks of June. NASS also publishes an independent estimate of the number of farms from the quinquennial Census&amp;nbsp;of Agriculture. Studies conducted by NASS have shown that farm number estimates from the JAS can be biased,&amp;nbsp;mainly due to misclassification of agricultural tracts during the pre-screening and data collection processes. To adjust&amp;nbsp;for the bias, NASS has developed a capture-recapture model that uses NASS’s list frame as the second sample, where&amp;nbsp;estimation is performed based on records in the JAS with matches in the list frame. In the current paper, we describe&amp;nbsp;an alternative capture-recapture approach that uses all available data from the JAS and the Census of Agriculture to&amp;nbsp;correct for biases due to misclassification and to produce more stable farm number estimates.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">W. Cui</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multidimensionality in the Performance-based Online Reading Comprehension Assessment</style></title></titles><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</style></author><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Coiro, J.</style></author><author><style face="normal" font="default" size="100%">Forzani, E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Performance Characteristics of Three Formats for Assessing Internet Research Skills in Science</style></title></titles><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">W. Cui</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Psychometric Invariance of Online Reading Comprehension Assessment across Measurement Conditions</style></title></titles><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Benecha, H.</style></author><author><style face="normal" font="default" size="100%">Toppin, K.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Restricted Multinomial Regression for a Triple-System Estimation with List Dependence</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">BigData</style></keyword><keyword><style  face="normal" font="default" size="100%">Capture</style></keyword><keyword><style  face="normal" font="default" size="100%">DataScience</style></keyword><keyword><style  face="normal" font="default" size="100%">Dependence</style></keyword><keyword><style  face="normal" font="default" size="100%">Estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword><keyword><style  face="normal" font="default" size="100%">Probability</style></keyword><keyword><style  face="normal" font="default" size="100%">Triple-System</style></keyword><keyword><style  face="normal" font="default" size="100%">Weights</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Sartore_RestMultiReg_TSE_20170901.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The National Agricultural Statistics Service (NASS) conducts the U.S. Census of Agriculture every&amp;nbsp;five years. In 2012, NASS began using a capture-recapture approach to adjust the Census estimates&amp;nbsp;for under-coverage, non-response, and misclassification. This requires two independent samples.&amp;nbsp;NASS has kept its Census Mailing List (CML) independent from its area frame, which is used for the&amp;nbsp;June Area Survey (JAS) every June. NASS is exploring the use of web-scraping to develop a third&amp;nbsp;list-frame (TL) that would be independent of the CML and the area frame. In this paper, a Triple-System Estimation (TSE) methodology based on regularized multinomial regression is proposed to&amp;nbsp;investigate for possible dependence between the CML and the TF. A simulation study is performed&amp;nbsp;to compare the performance of the estimator based on the proposed methodology, which can take&amp;nbsp;into account the frame dependence with others already presented in the literature.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Troia, G. A.</style></author><author><style face="normal" font="default" size="100%">Olinghouse, N. G.</style></author><author><style face="normal" font="default" size="100%">Wilson, J.</style></author><author><style face="normal" font="default" size="100%">Stewart, K. O.</style></author><author><style face="normal" font="default" size="100%">Mo, Y.</style></author><author><style face="normal" font="default" size="100%">Hawkins, L.</style></author><author><style face="normal" font="default" size="100%">Kopke, R.A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Common Core Writing Standards: A descriptive study of content and alignment with a sample of former state standards</style></title><secondary-title><style face="normal" font="default" size="100%">Reading Horizons</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Fabbri, P.</style></author><author><style face="normal" font="default" size="100%">Gaetan, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">spMC: an R-package for 3D Lithological Reconstructions Based on Spatial Markov Chains</style></title><secondary-title><style face="normal" font="default" size="100%">Computers and Geosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0098300416301479</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">94</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil prediction/simulation in a plain site of the NE Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, in spMC package the most known estimation/simulation methods as indicator Kriging and CoKriging were implemented, but also most advanced methods such as path methods and Bayesian procedure exploiting the maximum entropy. Because the spMC package was thought for intensive geostatistical computations, part of the code is implemented with parallel computing via the OpenMP constructs, allowing to deal with more than five lithologies, but trying to keep a computational efficiency. A final analysis of this computational efficiency&amp;nbsp;of spMC compares the prediction/simulation results using different numbers of CPU cores, considering the example data set of the case study available in the package.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">40-47</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schifeling, T.</style></author><author><style face="normal" font="default" size="100%">Cheng, C.</style></author><author><style face="normal" font="default" size="100%">Jerome Reiter</style></author><author><style face="normal" font="default" size="100%">Hillygus, D.C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accounting for nonignorable unit nonresponse and attrition in panel studies with refreshment samples</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Survey Statistics and Methodology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">18 August 2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">265–295</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Susan Abbatiello</style></author><author><style face="normal" font="default" size="100%">Birgit Schilling</style></author><author><style face="normal" font="default" size="100%">D.R. Mani</style></author><author><style face="normal" font="default" size="100%">L.I. Shilling</style></author><author><style face="normal" font="default" size="100%">S.C. Hall</style></author><author><style face="normal" font="default" size="100%">B. McLean</style></author><author><style face="normal" font="default" size="100%">M. Albetolle</style></author><author><style face="normal" font="default" size="100%">S. Allen</style></author><author><style face="normal" font="default" size="100%">M. Burgess</style></author><author><style face="normal" font="default" size="100%">M.P. Cusack</style></author><author><style face="normal" font="default" size="100%">M Gosh</style></author><author><style face="normal" font="default" size="100%">V Hedrick</style></author><author><style face="normal" font="default" size="100%">J.M. Held</style></author><author><style face="normal" font="default" size="100%">H.D. Inerowicz</style></author><author><style face="normal" font="default" size="100%">A. Jackson</style></author><author><style face="normal" font="default" size="100%">H. Keshishian</style></author><author><style face="normal" font="default" size="100%">C.R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Makowski L</style></author><author><style face="normal" font="default" size="100%">Mesri M</style></author><author><style face="normal" font="default" size="100%">Rodriguez H</style></author><author><style face="normal" font="default" size="100%">Rudnick P</style></author><author><style face="normal" font="default" size="100%">Sadowski P</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Shaddox K</style></author><author><style face="normal" font="default" size="100%">Skates SJ</style></author><author><style face="normal" font="default" size="100%">Kuhn E</style></author><author><style face="normal" font="default" size="100%">Smith D</style></author><author><style face="normal" font="default" size="100%">Whiteaker, JR</style></author><author><style face="normal" font="default" size="100%">Whitwell C</style></author><author><style face="normal" font="default" size="100%">Zhang S</style></author><author><style face="normal" font="default" size="100%">Borchers CH</style></author><author><style face="normal" font="default" size="100%">Fisher SJ</style></author><author><style face="normal" font="default" size="100%">Gibson BW</style></author><author><style face="normal" font="default" size="100%">Liebler DC</style></author><author><style face="normal" font="default" size="100%">M.J. McCoss</style></author><author><style face="normal" font="default" size="100%">Neubert TA</style></author><author><style face="normal" font="default" size="100%">Paulovich AG</style></author><author><style face="normal" font="default" size="100%">Regnier FE</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma.</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular Cell Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">2357-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data, we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to subnanogram/ml sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and interlaboratory reproducibility was &amp;lt;20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy-isotope-labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an interlaboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality control measures, enables sensitive, specific, reproducible, and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Feng, X.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Xia, J-Q</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calibration using Constrained Smoothing with Application to Mass Spectrometry Data</style></title><secondary-title><style face="normal" font="default" size="100%">Biometrics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291541-0420</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">70</style></volume><pages><style face="normal" font="default" size="100%">398-408</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">398</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J. Lane</style></author><author><style face="normal" font="default" size="100%">V. Stodden</style></author><author><style face="normal" font="default" size="100%">H. Nissenbaum</style></author><author><style face="normal" font="default" size="100%">S. Bender</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Confidentiality and Data Access in the Use of Big Data: Theory and Practical Approaches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Cambridge University Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">Analytical frameworks for data release: A statistical view</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. A. Rudnick</style></author><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">E. Yan</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">S. E. Stein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular &amp; Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">1341-1351</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Forzani, E.</style></author><author><style face="normal" font="default" size="100%">Burlingame, C.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.</style></author><author><style face="normal" font="default" size="100%">Coiro, J.</style></author><author><style face="normal" font="default" size="100%">Kennedy, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Neuman, S. B.</style></author><author><style face="normal" font="default" size="100%">Gambrell, L.B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The New Literacies of Online Research and Comprehension: Assessing and Preparing Students for the 21st Century with Common Core State Standards</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">International Reading Association</style></publisher><pages><style face="normal" font="default" size="100%">to appear</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">to appear</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">M. C. Chambers</style></author><author><style face="normal" font="default" size="100%">L. J. Vega-Montoto</style></author><author><style face="normal" font="default" size="100%">D. M. Bunk</style></author><author><style face="normal" font="default" size="100%">S. E. Stein</style></author><author><style face="normal" font="default" size="100%">D. Tabb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">QC Metrics from CPTAC Raw LC-MS/MS Data Interpreted through Multivariate Statistics</style></title><secondary-title><style face="normal" font="default" size="100%">Analytical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://pubs.acs.org/doi/pdf/10.1021/ac4034455</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">86</style></volume><pages><style face="normal" font="default" size="100%">2497 − 2509</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div&gt;Shotgun proteomics experiments integrate a complex sequence of processes, any of which can introduce variability. Quality metrics computed from LC-MS/MS data have relied upon identifying MS/MS scans, but a new mode for the QuaMeter software produces metrics that are independent of identifications. Rather than evaluating each metric independently, we have created a robust multivariate statistical toolkit that accommodates the correlation structure of these metrics and allows for hierarchical relationships among data sets. The framework enables visualization and structural assessment of variability. Study 1 for the Clinical Proteomics Technology Assessment for Cancer (CPTAC), which analyzed three replicates of two common samples at each of two time points among 23 mass spectrometers in nine laboratories, provided the data to demonstrate this framework, and CPTAC Study 5 provided data from complex lysates under Standard Operating Procedures (SOPs) to complement these findings. Identification-independent quality metrics enabled the differentiation of sites and run-times through robust principalcomponents analysis and subsequent factor analysis. Dissimilarity metrics revealed outliers in performance, and a nested ANOVA model revealed the extent to which all metrics or individual metrics were impacted by mass spectrometer and run time. Study 5 data revealed that even when SOPs have been applied, instrument-dependent variability remains prominent, although it may bereduced, while within-site variability is reduced significantly. Finally, identification-independent quality metrics were shown to bepredictive of identification sensitivity in these data sets. QuaMeter and the associated multivariate framework are available from http://fenchurch.mc.vanderbilt.edu and http://homepages.uc.edu/~wang2x7/, respectively&lt;/div&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang K</style></author><author><style face="normal" font="default" size="100%">Hughes-Oliver JM</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of high-dimensional structure-activity screening datasets using the optimal bit string Tree</style></title><secondary-title><style face="normal" font="default" size="100%">Technomet</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug discovery</style></keyword><keyword><style  face="normal" font="default" size="100%">High throughput screening</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">QSAR</style></keyword><keyword><style  face="normal" font="default" size="100%">Simulated annealing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">55</style></volume><pages><style face="normal" font="default" size="100%">161-173</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We propose a new classification method called the Optimal Bit String Tree (OBSTree) to identify quantitative structure-activity relationships (QSARs). The method introduces the concept of a chromosome to describe the presence/absence context of a combination of descriptors. A descriptor set and its optimal chromosome form the splitting variable. A new stochastic searching scheme that contains a weighted sampling scheme, simulated annealing, and a trimming procedure optimizes the choice of splitting variable. Simulation studies and an application to screening monoamine oxidase inhibitors show that OBSTree is advantageous in accurately and effectively identifying QSAR rules and finding different classes of active compounds. Details of the algorithm, SAS code, and simulated and real datasets are available online as supplementary materials.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">W. Cui</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining NAEP Items into a Baseline Offline Reading Assessment</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">U. S. Department of Education</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">ORCA Technical Report</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abbatiello, S.</style></author><author><style face="normal" font="default" size="100%">Feng, X.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Mani, DR</style></author><author><style face="normal" font="default" size="100%">Schilling, B</style></author><author><style face="normal" font="default" size="100%">Maclean, B</style></author><author><style face="normal" font="default" size="100%">Zimmerman, LJ</style></author><author><style face="normal" font="default" size="100%">Cusack, MP</style></author><author><style face="normal" font="default" size="100%">Hall, SC</style></author><author><style face="normal" font="default" size="100%">Addona, T</style></author><author><style face="normal" font="default" size="100%">Allen, S</style></author><author><style face="normal" font="default" size="100%">Dodder, NG</style></author><author><style face="normal" font="default" size="100%">Ghosh, M</style></author><author><style face="normal" font="default" size="100%">Held, JM</style></author><author><style face="normal" font="default" size="100%">Hedrick, V</style></author><author><style face="normal" font="default" size="100%">Inerowicz, HD</style></author><author><style face="normal" font="default" size="100%">Jackson, A</style></author><author><style face="normal" font="default" size="100%">Keshishian, H</style></author><author><style face="normal" font="default" size="100%">Kim, JW</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Riley, CP</style></author><author><style face="normal" font="default" size="100%">Rudnick, P</style></author><author><style face="normal" font="default" size="100%">Sadowski, P</style></author><author><style face="normal" font="default" size="100%">Shaddox, K</style></author><author><style face="normal" font="default" size="100%">Smith, D</style></author><author><style face="normal" font="default" size="100%">Tomazela, D</style></author><author><style face="normal" font="default" size="100%">Wahlander, A</style></author><author><style face="normal" font="default" size="100%">Waldemarson, S</style></author><author><style face="normal" font="default" size="100%">Whitwell, CA</style></author><author><style face="normal" font="default" size="100%">You, J</style></author><author><style face="normal" font="default" size="100%">Zhang, S</style></author><author><style face="normal" font="default" size="100%">Kinsinger, CR</style></author><author><style face="normal" font="default" size="100%">Mesri, M</style></author><author><style face="normal" font="default" size="100%">Rodriguez, H</style></author><author><style face="normal" font="default" size="100%">Borchers, CH</style></author><author><style face="normal" font="default" size="100%">Buck, C</style></author><author><style face="normal" font="default" size="100%">Fisher, SJ</style></author><author><style face="normal" font="default" size="100%">Gibson, BW</style></author><author><style face="normal" font="default" size="100%">Liebler, D</style></author><author><style face="normal" font="default" size="100%">Maccoss, M</style></author><author><style face="normal" font="default" size="100%">Neubert, TA</style></author><author><style face="normal" font="default" size="100%">Paulovich, A</style></author><author><style face="normal" font="default" size="100%">Regnier, F</style></author><author><style face="normal" font="default" size="100%">Skates, SJ</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Wang, M</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design, Implementation and Multisite Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS)</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular and Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">2623-2639</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation &amp;lt;0.15, peak width coefficient of variation &amp;lt;0.15, standard deviation of RT &amp;lt;0.15 min (9 s), and the RT drift &amp;lt;0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhou, Y-C.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Functional Data Based Biomarker for Modeling Cardiovascular Behavior</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Medicine</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">electrocardiogram</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</style></keyword><keyword><style  face="normal" font="default" size="100%">ventricular repolarization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">153-164</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cardiac safety assessment in drug development concerns the ventricular repolarization (represented by electrocardiogram (ECG) T-wave) abnormalities of a cardiac cycle, which are widely believed to be linked with torsades de pointes, a potentially life-threatening arrhythmia. The most often used biomarker for such abnormalities is the prolongation of the QT interval, which relies on the correct annotation of onset of QRS complex and offset of T-wave on ECG. A new biomarker generated from a functional data-based methodology is developed to quantify the T-wave morphology changes from placebo to drug interventions. Comparisons of T-wave-form characters through a multivariate linear mixed model are made to assess cardiovascular risk of drugs. Data from a study with 60 subjects participating in a two-period placebo-controlled crossover trial with repeat ECGs obtained at baseline and 12 time points after interventions are used to illustrate this methodology; different types of wave form changes were characterized and motivated further investigation.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Neuman, S.</style></author><author><style face="normal" font="default" size="100%">Gambrell, L.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The New Literacies of Online Research and Comprehension: Assessing and Preparing Students for the 21st Century with Common Core State Standards</style></title><secondary-title><style face="normal" font="default" size="100%">Quality Reading Instruction in the Age of Common Core Standards</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">International Reading Association</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">16</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hughes-Oliver JM</style></author><author><style face="normal" font="default" size="100%">Brooks A</style></author><author><style face="normal" font="default" size="100%">Welch W</style></author><author><style face="normal" font="default" size="100%">Khaldei MG</style></author><author><style face="normal" font="default" size="100%">Hawkins DM</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Patil K</style></author><author><style face="normal" font="default" size="100%">Howell GW</style></author><author><style face="normal" font="default" size="100%">Ng RT</style></author><author><style face="normal" font="default" size="100%">Chu MT</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ChemModLab: A web-based cheminromates modeling laboratory</style></title><secondary-title><style face="normal" font="default" size="100%">Cheminformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">61-81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;ChemModLab, written by the ECCR @ NCSU consortium under NIH support, is a toolbox for fitting and assessing quantitative structure-activity relationships (QSARs). Its elements are: a cheminformatic front end used to supply molecular descriptors for use in modeling; a set of methods for fitting models; and methods for validating the resulting model. Compounds may be input as structures from which standard descriptors will be calculated using the freely available cheminformatic front end PowerMV; PowerMV also supports compound visualization. In addition, the user can directly input their own choices of descriptors, so the capability for comparing descriptors is effectively unlimited. The statistical methodologies comprise a comprehensive collection of approaches whose validity and utility have been accepted by experts in the fields. As far as possible, these tools are implemented in open-source software linked into the flexible R platform, giving the user the capability of applying many different QSAR modeling methods in a seamless way. As promising new QSAR methodologies emerge from the statistical and data-mining communities, they will be incorporated in the laboratory. The web site also incorporates links to public-domain data sets that can be used as test cases for proposed new modeling methods. The capabilities of ChemModLab are illustrated using a variety of biological responses, with different modeling methodologies being applied to each. These show clear differences in quality of the fitted QSAR model, and in computational requirements. The laboratory is web-based, and use is free. Researchers with new assay data, a new descriptor set, or a new modeling method may readily build QSAR models and benchmark their results against other findings. Users may also examine the diversity of the molecules identified by a QSAR model. Moreover, users have the choice of placing their data sets in a public area to facilitate communication with other researchers; or can keep them hidden to preserve confidentiality.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Current and emerging design and data analysis approaches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">APA Handbook of Educational Psychology, American Psychological Association</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Young, L.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data, Statistics and Controversy: Making Scientific Data Intelligible</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics, Politics and Policy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data availability</style></keyword><keyword><style  face="normal" font="default" size="100%">Daubert rule</style></keyword><keyword><style  face="normal" font="default" size="100%">inference verification</style></keyword><keyword><style  face="normal" font="default" size="100%">meta-data</style></keyword><keyword><style  face="normal" font="default" size="100%">proprietary data</style></keyword><keyword><style  face="normal" font="default" size="100%">publication bias</style></keyword><keyword><style  face="normal" font="default" size="100%">reuse of data</style></keyword><keyword><style  face="normal" font="default" size="100%">secondary analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">1-20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Making published, scientific research data publicly available can benefit scientists and policy makers only if there is sufficient information for these data to be intelligible. Thus the necessary meta-data go beyond the scientific, technological detail and extend to the statistical approach and methodologies applied to these data. The statistical principles that give integrity to researchers’ analyses and interpretations of their data require documentation. This is true when the intent is to verify or validate the published research findings; it is equally true when the intent is to utilize the scientific data in conjunction with other data or new experimental data to explore complex questions; and it is profoundly important when the scientific results and interpretations are taken outside the world of science to establish a basis for policy, for legal precedent or for decision-making. When research draws on already public data bases, e.g., a large federal statistical data base or a large scientific data base, selection of data for analysis, whether by selection (subsampling) or by aggregating, is specific to that research so that this (statistical) methodology is a crucial part of the meta-data. Examples illustrate the role of statistical meta-data in the use and reuse of these public datasets and the impact on public policy and precedent.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Karr Alan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deming, data and observational studies. A process out of control and needing fixing</style></title><secondary-title><style face="normal" font="default" size="100%">Significance</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">observational studies</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">116-120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Any claim coming from an observational study is most likely to be wrong.? Startling, but true. Coffee causes pancreatic cancer. Type A personality causes heart attacks. Trans-fat is a killer. Women who eat breakfast cereal give birth to more boys. All these claims come from observational studies; yet when the studies are carefully examined, the claimed links appear to be incorrect. What is going wrong? Some have suggested that the scientific method is failing, that nature itself is playing tricks on us. But it is our way of studying nature that is broken and that urgently needs mending, say S. Stanley Young and Alan Karr; and they propose a strategy to fix it.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author><author><style face="normal" font="default" size="100%">Deborah Nolan</style></author><author><style face="normal" font="default" size="100%">Keith Soper</style></author><author><style face="normal" font="default" size="100%">Cliff Spiegelman</style></author><author><style face="normal" font="default" size="100%">Linda J. Young</style></author><author><style face="normal" font="default" size="100%">Katrina L. Kelner</style></author><author><style face="normal" font="default" size="100%">Robert A. Moffitt</style></author><author><style face="normal" font="default" size="100%">Ani Thakar</style></author><author><style face="normal" font="default" size="100%">Jordan Raddick</style></author><author><style face="normal" font="default" size="100%">Edward J. Ungvarsky</style></author><author><style face="normal" font="default" size="100%">Richard W. Carlson</style></author><author><style face="normal" font="default" size="100%">Rolf Apweiler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Make research data public? - Not always so simple: A Dialogue for statisticians and science editors</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">41-50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a dialogue about policies and practicalities of requiring published research to be accompanied by publication of the research data. This dialogue carried beyond the broad issues of the advisability, the intellectual integrity, the scientific exigencies to the relevance of these issues to statistics as a discipline and the relevance of statistics, from inference to modeling to data exploration, to science and social science policies on these issues.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pauley, L.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Engel, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Studying the Reliability and Validity of Test Scores for Mathematical and Spatial Reasoning Tasks for Engineering Students</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings, American Society for Engineering Education</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Beasley CM Jr</style></author><author><style face="normal" font="default" size="100%">Benson C</style></author><author><style face="normal" font="default" size="100%">Xia JQ</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Haber H</style></author><author><style face="normal" font="default" size="100%">Mitchell MI</style></author><author><style face="normal" font="default" size="100%">Loghin C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Systematic decrements in QTc between the first and second day of contiguous daily ECG recordings under controlled conditions</style></title><secondary-title><style face="normal" font="default" size="100%">PACE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ECG</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">1116-1127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;BACKGROUND: Many thorough QT (TQT) studies use a baseline day and double delta analysis to account for potential diurnal variation in QTc. However, little is known about systematic changes in the QTc across contiguous days when normal volunteers are brought into a controlled inpatient environment.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xia, J.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Feng, X.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variance Component Analysis of a Multi-Site Study of Multiple Reaction Monitoring Measurements of Peptides and Proteins in Human Plasma</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS1</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">analysis of Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">blood plasma</style></keyword><keyword><style  face="normal" font="default" size="100%">experimental design</style></keyword><keyword><style  face="normal" font="default" size="100%">Instrument calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">linear regression analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">peptides</style></keyword><keyword><style  face="normal" font="default" size="100%">plasma proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">proteomic databases</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">e14590</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan A. Carr</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk.</style></author><author><style face="normal" font="default" size="100%">Henry Rodriguez</style></author><author><style face="normal" font="default" size="100%">Zivana Tezak</style></author><author><style face="normal" font="default" size="100%">Mehdi Mesri</style></author><author><style face="normal" font="default" size="100%">Daniel C. Liebler</style></author><author><style face="normal" font="default" size="100%">Susan J. Fisher</style></author><author><style face="normal" font="default" size="100%">Paul Tempst</style></author><author><style face="normal" font="default" size="100%">Tara Hiltke</style></author><author><style face="normal" font="default" size="100%">Larry G. Kessler</style></author><author><style face="normal" font="default" size="100%">Christopher R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Reena Philip</style></author><author><style face="normal" font="default" size="100%">David F. Ransohoff</style></author><author><style face="normal" font="default" size="100%">Steven J. Skates</style></author><author><style face="normal" font="default" size="100%">Fred E. Regnier</style></author><author><style face="normal" font="default" size="100%">N. Leigh Anderson</style></author><author><style face="normal" font="default" size="100%">Elizabeth Mansfield</style></author><author><style face="normal" font="default" size="100%">on behalf of the Workshop Participants</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analytical Validation of Proteomic-Based Multiplex Assays: A Workshop Report by the NCI-FDA Interagency Oncology Task Force on Molecular Diagnostics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Clinical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">237-243</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Clinical proteomics has the potential to enable the early detection of cancer through the development of multiplex assays that can inform clinical decisions. However, there has been some uncertainty among translational researchers and developers as to the specific analytical measurement criteria needed to validate protein-based multiplex assays. To begin to address the causes of this uncertainty, a day-long workshop titled “Interagency Oncology Task Force Molecular Diagnostics Workshop” was held in which members of the proteomics and regulatory communities discussed many of the analytical evaluation issues that the field should address in development of protein-based multiplex assays for clinical use. This meeting report explores the issues raised at the workshop and details the recommendations that came out of the day’s discussions, such as a workshop summary discussing the analytical evaluation issues that specific proteomic technologies should address when seeking US Food and Drug Administration approval.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pauley, L.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Engel, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Constructing mathematical and spatial-reasoning measures for engineering students</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings, American Society for Engineering Education</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhou, Y-C.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Marking the Ends of T-waves: Algorithms and Experts</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Biopharmaceutical Research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">359-367</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval endpoints. However, selected beats of data show that the difference between two experts’ marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave. This article develops a Bayesian approach combining both slope and curvature information. We show that the difference between the automatic Bayesian marks and either of the experts’ marks is not statistically larger than the difference between two experts’ marks, thus this approach is successful in closely approximating the experts’ results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhou, Y-C.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Marking the Ends of T-waves: Algorithms and Experts</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Biopharmaceutical Research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">359-367</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval endpoints. However, selected beats of data show that the difference between two experts’ marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave. This article develops a Bayesian approach combining both slope and curvature information. We show that the difference between the automatic Bayesian marks and either of the experts’ marks is not statistically larger than the difference between two experts’ marks, thus this approach is successful in closely approximating the experts’ results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</style></author><author><style face="normal" font="default" size="100%">Engel, R.</style></author><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">Gunning, P.</style></author><author><style face="normal" font="default" size="100%">Fleming, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Psychometric and Statistical Modeling for the Study of Retention and Graduation in Undergraduate Engineering</style></title><secondary-title><style face="normal" font="default" size="100%">Social Statistics and Higher Education Conference Volume</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical Careers in US Government Science Agencies</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">complex system models</style></keyword><keyword><style  face="normal" font="default" size="100%">engineering statistics</style></keyword><keyword><style  face="normal" font="default" size="100%">high-dimensional data</style></keyword><keyword><style  face="normal" font="default" size="100%">History of statistics</style></keyword><keyword><style  face="normal" font="default" size="100%">metrology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">443-453</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The role of statistics in those U.S. government agencies that focus on progress in science and engineering became prominent at the end of the Second World War. The success of statistics in that historical period came from the power of statistics to enable science to advance more rapidly and with great assurance in the interpretation of experimental results. Over the past three quarters of a century, technology has changed both the practice of science and the practice of statistics. However, the comparative advantage of statistics still rests in the ability to achieve greater precision with fewer errors and a deeper understanding. Examples illustrate some of the challenges that complex science now presents to statisticians, demanding both creativity and technical skills.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhou, Y-C.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional Data Analytic Approach of Modeling ECG T-wave shape to Measure Cardiovascular Behavior</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of Applied Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cardiac safety</style></keyword><keyword><style  face="normal" font="default" size="100%">ECG T-wave</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</style></keyword><keyword><style  face="normal" font="default" size="100%">T-wave morphology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">1382-1402</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The T-wave of an electrocardiogram (ECG) represents the ventricular repolarization that is critical in restoration of the heart muscle to a pre-contractile state prior to the next beat. Alterations in the T-wave reflect various cardiac conditions; and links between abnormal (prolonged) ventricular repolarization and malignant arrhythmias have been documented. Cardiac safety testing prior to approval of any new drug currently relies on two points of the ECG waveform: onset of the Q-wave and termination of the T-wave; and only a few beats are measured. Using functional data analysis, a statistical approach extracts a common shape for each subject (reference curve) from a sequence of beats, and then models the deviation of each curve in the sequence from that reference curve as a four-dimensional vector. The representation can be used to distinguish differences between beats or to model shape changes in a subject’s T-wave over time. This model provides physically interpretable parameters characterizing T-wave shape, and is robust to the determination of the endpoint of the T-wave. Thus, this dimension reduction methodology offers the strong potential for definition of more robust and more informative biomarkers of cardiac abnormalities than the QT (or QT corrected) interval in current use.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhou, Y-C.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional Data Analytic Approach of Modeling ECG T-wave shape to Measure Cardiovascular Behavior</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of Applied Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cardiac safety</style></keyword><keyword><style  face="normal" font="default" size="100%">ECG T-wave</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</style></keyword><keyword><style  face="normal" font="default" size="100%">T-wave morphology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">1382-1402</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The T-wave of an electrocardiogram (ECG) represents the ventricular repolarization that is critical in restoration of the heart muscle to a pre-contractile state prior to the next beat. Alterations in the T-wave reflect various cardiac conditions; and links between abnormal (prolonged) ventricular repolarization and malignant arrhythmias have been documented. Cardiac safety testing prior to approval of any new drug currently relies on two points of the ECG waveform: onset of the Q-wave and termination of the T-wave; and only a few beats are measured. Using functional data analysis, a statistical approach extracts a common shape for each subject (reference curve) from a sequence of beats, and then models the deviation of each curve in the sequence from that reference curve as a four-dimensional vector. The representation can be used to distinguish differences between beats or to model shape changes in a subject’s T-wave over time. This model provides physically interpretable parameters characterizing T-wave shape, and is robust to the determination of the endpoint of the T-wave. Thus, this dimension reduction methodology offers the strong potential for definition of more robust and more informative biomarkers of cardiac abnormalities than the QT (or QT corrected) interval in current use.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Privacy-preserving analysis of vertically partitioned data using secure matrix products</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">125-138</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Task Force Report on Computer Adaptive Testing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">National Center for Education Statistics</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Bang H</style></author><author><style face="normal" font="default" size="100%">Oktay K</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cereal-induced gender selection? Most likely a multiple testing false positive</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - Royal Society B</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://rspb.royalsocietypublishing.org/content/276/1660/1211.full</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">276</style></volume><pages><style face="normal" font="default" size="100%">1211-1212</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Chen, Hsinchun</style></author><author><style face="normal" font="default" size="100%">Reid, Edna</style></author><author><style face="normal" font="default" size="100%">Sinai, Joshua</style></author><author><style face="normal" font="default" size="100%">Silke, Andrew</style></author><author><style face="normal" font="default" size="100%">Ganor, Boaz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Homeland Insecurity</style></title><secondary-title><style face="normal" font="default" size="100%">Terrorism Informatics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Integrated Series In Information Systems</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-0-387-71613-8_10</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">197-218</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-71612-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. There have also been claims that prospective datamining could be used to find the “signature” of terrorist cells embedded in larger networks. We present an overview of why the public has concerns about such activities and describe some proposals for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literatures on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of “selective revelation” and their confidentiality implications.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young SS</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Low-fat dietary pattern and cancer incidence in the Women’s Health Initiative Dietary Modification Randomized Controlled Trial</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of National Cancer Institute</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://jnci.oxfordjournals.org/content/100/4/284.1.extract#</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">100</style></volume><pages><style face="normal" font="default" size="100%">284</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. T. Banks</style></author><author><style face="normal" font="default" size="100%">H. K. Nguyen</style></author><author><style face="normal" font="default" size="100%">J. R. Samuels, Jr.</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sensitivity to noise variance in a social network dynamics model</style></title><secondary-title><style face="normal" font="default" size="100%">Q. Applied Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">66</style></volume><pages><style face="normal" font="default" size="100%">233-247</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M.J. Bayarri</style></author><author><style face="normal" font="default" size="100%">J. Berger</style></author><author><style face="normal" font="default" size="100%">Garcia-Donato, G.</style></author><author><style face="normal" font="default" size="100%">Liu, F.</style></author><author><style face="normal" font="default" size="100%">R. Paulo</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Palomo, J.</style></author><author><style face="normal" font="default" size="100%">Walsh, D.</style></author><author><style face="normal" font="default" size="100%">J. Cafeo</style></author><author><style face="normal" font="default" size="100%">Parthasarathy, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer Model Validation with Functional Output</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of  Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">1874-190</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, X. S.</style></author><author><style face="normal" font="default" size="100%">Salloum, G.A.</style></author><author><style face="normal" font="default" size="100%">Chipman, H.A.</style></author><author><style face="normal" font="default" size="100%">Welch, W.J.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploration of cluster structure-activity relationship analysis in efficient high-throughput screening</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1206-1214</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Sequential screening has become increasingly popular in drug discovery. It iteratively builds quantitative structure-activity relationship (QSAR) models from successive high-throughput screens, making screening more effective and efficient. We compare cluster structure-activity relationship analysis (CSARA) as a QSAR method with recursive partitioning (RP), by designing three strategies for sequential collection and analysis of screening data. Various descriptor sets are used in the QSAR models to characterize chemical structure, including high-dimensional sets and some that by design have many variables not related to activity. The results show that CSARA outperforms RP. We also extend the CSARA method to deal with a continuous assay measurement.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. E. Fienberg</style></author><author><style face="normal" font="default" size="100%">Y. Nardi</style></author><author><style face="normal" font="default" size="100%">A. Slavkovic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure logistic regression with distributed databases</style></title><secondary-title><style face="normal" font="default" size="100%">Bulletin of International Statistics Institute</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Rukhin, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistics in metrology: International key comparisons and interlaboratory studies</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Data Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">393-412</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Murali Haran</style></author><author><style face="normal" font="default" size="100%">Alan Karr</style></author><author><style face="normal" font="default" size="100%">Michael Last</style></author><author><style face="normal" font="default" size="100%">Alessandro Orso</style></author><author><style face="normal" font="default" size="100%">Adam A. Porter</style></author><author><style face="normal" font="default" size="100%">Ashish Sanil</style></author><author><style face="normal" font="default" size="100%">Sandro Fouché</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Techniques for classifying executions of deployed software to support software engineering tasks</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE TRANSACTIONS ON SOFTWARE ENGINEERING</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">287-304</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author><author><style face="normal" font="default" size="100%">David L. Banks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data quality: A statistical perspective</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Methodology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">137–173</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C. N. Kohnen</style></author><author><style face="normal" font="default" size="100%">A. Oganyan</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A framework for evaluating the utility of data altered to protect confidentiality</style></title><secondary-title><style face="normal" font="default" size="100%">The American Statistician</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">60</style></volume><pages><style face="normal" font="default" size="100%">224-232</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Feng J.</style></author><author><style face="normal" font="default" size="100%">Sanil A</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PharmID: Pharmacophore identification using Gibbs sampling</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">1352-1359</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The binding of a small molecule to a protein is inherently a 3D matching problem. As crystal structures are not available for most drug targets, there is a need to be able to infer from bioassay data the key binding features of small molecules and their disposition in space, the pharmacophore. Fingerprints of 3D features and a modification of Gibbs sampling to align a set of known flexible ligands, where all compounds are active, are used to discern possible pharmacophores. A clique detection method is used to map the features back onto the binding conformations. The complete algorithm is described in detail, and it is shown that the method can find common superimposition for several test data sets. The method reproduces answers very close to the crystal structure and literature pharmacophores in the examples presented. The basic algorithm is relatively fast and can easily deal with up to 100 compounds and tens of thousands of conformations. The algorithm is also able to handle multiple binding mode problems, which means it can superimpose molecules within the same data set according to two different sets of binding features. We demonstrate the successful use of this algorithm for multiple binding modes for a set of D2 and D4 ligands.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, N.-F.</style></author><author><style face="normal" font="default" size="100%">Strawderman, W.</style></author><author><style face="normal" font="default" size="100%">Liu, H.-k.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical analysis for multiple artifact problem in key comparisons with linear trends</style></title><secondary-title><style face="normal" font="default" size="100%">Metrologia</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational physics</style></keyword><keyword><style  face="normal" font="default" size="100%">instrumentation and measurement</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">21-26</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A statistical analysis for key comparisons with linear trends and multiple artefacts is proposed. This is an extension of a previous paper for a single artefact. The approach has the advantage that it is consistent with the no-trend case. The uncertainties for the key comparison reference value and the degrees of equivalence are also provided. As an example, the approach is applied to key comparison CCEM–K2.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Remlinger KS</style></author><author><style face="normal" font="default" size="100%">Hughes-Oliver JM</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Lam RL</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical design of pools using optimal coverage and minimal collision</style></title><secondary-title><style face="normal" font="default" size="100%">Technom</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Pharmaceutical industry</style></keyword><keyword><style  face="normal" font="default" size="100%">Pooled data</style></keyword><keyword><style  face="normal" font="default" size="100%">Pooling</style></keyword><keyword><style  face="normal" font="default" size="100%">Screening</style></keyword><keyword><style  face="normal" font="default" size="100%">Throughput</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">133-143</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The screening of large chemical libraries to identify new compounds can be simplified by testing compounds in pools. Two criteria for designing pools are considered: optimal coverage of the chemical space and minimal collision between compounds. Four pooling designs are applied to a public database and evaluated by determining how well the design criteria are met and whether the methods are able to find diverse active compounds. While one pool was outstanding, all designed pools outperformed randomly designed pools.&lt;/p&gt;
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Sanil</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data dissemination and disclosure limitation in a world without microdata: A risk-utility framework for remote access analysis servers</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">163-177</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data quality and data confidentiality for microdata: implications and strategies</style></title><secondary-title><style face="normal" font="default" size="100%">Bull. International Statistical Inst., 55th Session</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shanti Gomatam</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Swapping as a Decision Problem</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">categorical data</style></keyword><keyword><style  face="normal" font="default" size="100%">data confidentiality</style></keyword><keyword><style  face="normal" font="default" size="100%">Data swapping</style></keyword><keyword><style  face="normal" font="default" size="100%">data utility</style></keyword><keyword><style  face="normal" font="default" size="100%">disclosure risk</style></keyword><keyword><style  face="normal" font="default" size="100%">risk-utility frontier</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">635–655</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We construct a decision-theoretic formulation of data swapping in which quantitative measures of disclosure risk and data utility are employed to select one release from a possibly large set of candidates. The decision variables are the swap rate, swap attribute(s) and, possibly, constraints on the unswapped attributes. Risk–utility frontiers, consisting of those candidates not dominated in (risk, utility) space by any other candidate, are a principal tool for reducing the scale of the decision problem. Multiple measures of disclosure risk and data utility, including utility measures based directly on use of the swapped data for statistical inference, are introduced. Their behavior and resulting insights into the decision problem are illustrated using data from the U.S. Current Population Survey, the well-studied “Czech auto worker data” and data on schools and administrators generated by the U.S. National Center for Education Statistics.&lt;/p&gt;
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Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Privacy preserving regression modelling via distributed computation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><pages><style face="normal" font="default" size="100%">677-682</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. 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Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regression on distributed databases via secure multi-party computation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. dg.o 2004, National Conference on Digital Government Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><pages><style face="normal" font="default" size="100%">405-406</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C. N. Kohnen</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure regression for vertically partitioned, partially overlapping data</style></title><secondary-title><style face="normal" font="default" size="100%">ASA Proceedings 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. Gomatam</style></author><author><style face="normal" font="default" size="100%">C. Liu</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data swapping: A risk–utility framework and Web service implementation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. dg.o 2003, National Conference on Digital Government Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Digital Government Research Center</style></publisher><pages><style face="normal" font="default" size="100%">245-248</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Wang M</style></author><author><style face="normal" font="default" size="100%">Gu F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of diverse and focused combinatorial libraries using an alternating algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemistry Information and Computer Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">1916-1921</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hawkins, D.M.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, R.D.</style></author><author><style face="normal" font="default" size="100%">L. Liu</style></author><author><style face="normal" font="default" size="100%">Young. S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring blood spectra for signs of ovarian cancer</style></title><secondary-title><style face="normal" font="default" size="100%">Chance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">19-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">N. Siddiqui</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methods for Calibrating and Validating Stochastic Micro-Simulation Traffic Models</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">North Carolina State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Raleigh</style></pub-location><volume><style face="normal" font="default" size="100%">Masters</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">masters</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ashish Sanil</style></author><author><style face="normal" font="default" size="100%">Shanti Gomatam</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">NISS WebSwap: A Web Service for Data Swapping</style></title><secondary-title><style face="normal" font="default" size="100%">J. Statist. Software</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">2003</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Preserving confidentiality of high-dimensional tabular data: Statistical and computational issues</style></title><secondary-title><style face="normal" font="default" size="100%">STATISTICS AND COMPUTING</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">363–370</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Liu L</style></author><author><style face="normal" font="default" size="100%">Hawkins DM</style></author><author><style face="normal" font="default" size="100%">Ghosh S</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust singular value decomposition analysis of microarray data</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the National Academy of Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">100</style></volume><pages><style face="normal" font="default" size="100%">13167-13172</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Table servers protect confidentiality in tabular data releases</style></title><secondary-title><style face="normal" font="default" size="100%">Comm. ACM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">57–58</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Lee</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author><author><style face="normal" font="default" size="100%">J. Hernandez</style></author><author><style face="normal" font="default" size="100%">S. Karimi</style></author><author><style face="normal" font="default" size="100%">K. Litwin</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. Elmagarmid</style></author><author><style face="normal" font="default" size="100%">W. M. McIver</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Advances in Digital Government</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Kluwer</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston</style></pub-location><pages><style face="normal" font="default" size="100%">181-196</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4020-7067-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Internet provides an efficient mechanism for Federal agencies to distribute their data to the public. However, it is imperative that such data servers have built-in mechanisms to ensure that confidentiality of the data, and the privacy of individuals or establishments represented in the data, are not violated. We describe a prototype dissemination system developed for the National Agricultural Statistics Service that uses aggregation of adjacent geographical units as a confidentiality-preserving technique. We also outline a Bayesian approach to statistical analysis of the aggregated data.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">Web-based systems that disseminate information from data but preserve confidentiality</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boyce, David</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hewings, Geoffrey J.D.</style></author><author><style face="normal" font="default" size="100%">Sonis, Michael</style></author><author><style face="normal" font="default" size="100%">Boyce, David</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined Model of Interregional Commodity Flows on a Transportation Network</style></title><secondary-title><style face="normal" font="default" size="100%">Trade, Networks and Hierarchies</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Advances in Spatial Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-662-04786-6_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">29-40</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-07712-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This chapter is motivated by two ongoing research objectives of the author. The first concerns models of flows on transportation networks. Whether the subject is personal travel or freight transportation, representation of the transportation network is necessary to determine realistically interzonal/interregional travel/transportation costs. The methodological effort required to achieve such results is nontrivial, but the issues raised by such an attempt are enlightening and worthwhile. This insight is demonstrated once more by the models considered here.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jennifer Pittman Clarke</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">S. Stanley Young</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The construction and assessment of a statistical model for the prediction of protein assay data</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">729-741</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jennifer Pittman Clarke</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">S. Stanley Young</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The construction and assessment of a statistical model for the prediction of protein assay data</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">729-741</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M.J. Bayarri</style></author><author><style face="normal" font="default" size="100%">J. Berger</style></author><author><style face="normal" font="default" size="100%">D. Higdon</style></author><author><style face="normal" font="default" size="100%">M. Kottas</style></author><author><style face="normal" font="default" size="100%">R. Paulo</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author><author><style face="normal" font="default" size="100%">J. Cafeo</style></author><author><style face="normal" font="default" size="100%">J. Cavendish</style></author><author><style face="normal" font="default" size="100%">C. Lin</style></author><author><style face="normal" font="default" size="100%">J. Tu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Validating Computer Models</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Foundations for Modeling and Simulation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Society for Computer Simulation</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. Dobra</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal tabular releases from confidential data</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. dgo.2002, National Conference on Digital Government Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Julie Rapoport Corina–Maria</style></author><author><style face="normal" font="default" size="100%">Surendra P. Shah</style></author><author><style face="normal" font="default" size="100%">Bruce Ankenman</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Permeability of Cracked Steel Fiber–Reinforced Concrete</style></title><secondary-title><style face="normal" font="default" size="100%">ASCE J. Materials</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">355–358</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This research explores the relationship between permeability and crack width in cracked, steel fiber–reinforced concrete. In addition, it inspects the influence of steel fiber reinforcement on concrete permeability. The feedback–controlled splitting tension test (also known as the Brazilian test) is used to induce cracks of up to 500 microns (0.02in) in concrete specimens without reinforcement, and with steel fiber reinforcement volumes of both 0.5% and 1%. The cracks relax after induced cracking. The steel fibers decrease permeability of specimens with relaxed cracks larger than 100 microns. Keywords: permeability, fiber-reinforced concrete, steel fibers 1 NSF Center for Advanced Cement–Based Materials, Northwestern University, 2145 Sheridan Rd., Evanston, IL, 60208–4400, USA 2 Saint Gobain Technical Fabrics, P. Box 728, St. Catharines, Ontario, L2R-6Y3, Canada 3 Department of Industrial Engineering and Management Science, Northwestern University, 2145 Sheridan Rd., Evanston, IL.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Software Systems for Tabular Data Releases</style></title><secondary-title><style face="normal" font="default" size="100%">Int. Journal of Uncertainty, Fuzziness and Knowledge Based Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">529-544</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Nagui M. Rouphail</style></author><author><style face="normal" font="default" size="100%">B. Brian Park</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistically-Based Validation of Computer Simulation Models in Traffic Operations and Management</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Transportation and Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced traffic management systems</style></keyword><keyword><style  face="normal" font="default" size="100%">computer simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">CORSIM</style></keyword><keyword><style  face="normal" font="default" size="100%">model validation</style></keyword><keyword><style  face="normal" font="default" size="100%">transportation policy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The process of model validation is crucial for the use of computer simulation models in transportation policy, planning, and operations. This article lays out obstacles and issues involved in performing a validation. We describe a general process that emphasizes five essential ingredients for validation: context, data, uncertainty, feedback, and prediction. We use a test bed to generate specific (and general) questions as well as to give concrete form to answers and to the methods used in providing them. The traffic simulation model CORSIM serves as the test bed; we apply it to assess signal-timing plans on a street network of Chicago. The validation process applied in the test bed demonstrates how well CORSIM can reproduce field conditions, identifies flaws in the model, and shows how well CORSIM predicts performance under new (untried) signal conditions. We find that CORSIM, though imperfect, is effective with some restrictions in evaluating signal plans on urban networks.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">T.L. Graves</style></author><author><style face="normal" font="default" size="100%">A. Mockus</style></author><author><style face="normal" font="default" size="100%">P. Schuster</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variability of travel times on arterial streets: effects of signals and volume</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Record C</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">000-000</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephen G. Eick</style></author><author><style face="normal" font="default" size="100%">Paul Schuster</style></author><author><style face="normal" font="default" size="100%">Audris Mockus</style></author><author><style face="normal" font="default" size="100%">Todd L. Graves</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualizing Software Changes</style></title><secondary-title><style face="normal" font="default" size="100%">INTERACTIONS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">29–31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jaeyong Lee</style></author><author><style face="normal" font="default" size="100%">Christopher Holloman</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of aggregated data in survey sampling with application to fertilizer/pesticide usage surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Res. Official Statist</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">11–6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In many cases, the public release of survey or census data at fine geographical resolution (for example, counties) may endanger the confidentiality of respondents. A strategy for such cases is to aggregate neighboring regions into larger units that satisfy confidentiality requirements. An aggregation procedure employed in a prototype system for the US National Agricultural Statistics Service is used as context to investigate the impact of aggregation on statistical properties of the data. We propose a Bayesian simulation approach for the analysis of such aggregated data. As a consequence, we are able to specify the type of additional information (such as certain sample sizes) that needs to be released in order to enable the user to perform meaningful analyses with the aggregated data.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Byungkyu Park</style></author><author><style face="normal" font="default" size="100%">Nagui M. Rouphail</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Stochastic Signal Optimization Method Using Microsimulation</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Record</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">1748</style></volume><pages><style face="normal" font="default" size="100%">40-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A stochastic signal optimization method based on a genetic algorithm (GA-SOM) that interfaces with the microscopic simulation program CORSIM is assessed. A network in Chicago consisting of nine signalized intersections is used as an evaluation test bed. Taking CORSIM as the best representation of reality, the performance of the GA-SOM plan sets a ceiling on how good any (fixed) signal plan can be. An important aspect of this approach is its accommodations of variability. Also discussed is the robustness of an optimal plan under changes in demand. This benchmark is used to assess the best signal plan generated by TRANSYT-7F (T7F), Version 8.1, from among 12 reasonable strategies. The performance of the best T7F plan falls short of the benchmark on several counts, reflecting the need to account for variability in the highly stochastic system of traffic operations, which is not possible under the deterministic conditions intrinsic to T7F. As a sidelight, the performance of the GA-SOM plan within T7F is also computed and it is found to perform nearly as well as the optimum T7F plan.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">J. Rapoport</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined effect of cracking and water permeability of fiber-reinforced concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Concrete Under Severe Conditions, Proceedings of the Third International Conference on Concrete Under Severe Conditions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">71?78</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Hernandez</style></author><author><style face="normal" font="default" size="100%">S. Karimi</style></author><author><style face="normal" font="default" size="100%">J. Lee</style></author><author><style face="normal" font="default" size="100%">K. Litwin</style></author><author><style face="normal" font="default" size="100%">A. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Disseminating information but protecting confidentiality</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Computer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">36?37</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Park, B.</style></author><author><style face="normal" font="default" size="100%">N. M. Rouphail</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Traffic Simulation Model Validation Procedure Using CORSIM as a Test-Bed</style></title><secondary-title><style face="normal" font="default" size="100%">2001 International Symposium on Advanced Highway Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2001</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Tsuakawa, R. K.</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Propriety of posteriors with improper priors in hierarchical linear mixed models</style></title><secondary-title><style face="normal" font="default" size="100%">Statistica Sinica</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">77-95</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Web-Based Systems that Disseminate Information but Protect Confidential Data</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Digital Government. Kluwer, Amserdam</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Kluwer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author><author><style face="normal" font="default" size="100%">A. Elmagarmid</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Workshop Report: Affiliates Workshop on Data Quality</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">117</style></number><publisher><style face="normal" font="default" size="100%">National Institute of Statistical Sciences</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Lee</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Workshop Report: Workshop on Statistics and Information Technology</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">118</style></number><publisher><style face="normal" font="default" size="100%">National Institute of Statiatical Sciences</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young, S.Stanley</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gundertofte, Klaus</style></author><author><style face="normal" font="default" size="100%">Jørgensen, Flemming Steen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of a Large, High-Throughput Screening Data Using Recursive Partitioning</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular Modeling and Prediction of Bioactivity</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4615-4141-7_17</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">149-156</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4613-6857-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;As biological drug targets multiply through the human genome project and as the number of chemical compounds available for screening becomes very large, the expense of screening every compound against every target becomes prohibitive. We need to improve the efficiency of the drug screening process so that active compounds can be found for more biological targets and turned over to medicinal chemists for atom-by-atom optimization. We create a method for analysis of the very large, complex data sets coming from high throughput screening, and then integrate the analysis with the selection of compounds for screening so that the structure-activity rules derived from an initial compound set can be used to suggest additional compounds for screening. Cycles of screening and analysis become sequential screening rather than the mass screening of all available compounds. We extend the analysis method to deal with multivariate responses. Previously, a screening campaign might screen hundreds of thousands of compounds; sequential screening can cut the number of compounds screened by up to eighty percent. Sequential screening also gives SAR rules that can be used to mathematically screen compound collections or virtual chemical libraries.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Tsuakawa, R. K.</style></author><author><style face="normal" font="default" size="100%">Kim, H.</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Analysis of Mortality Rates with Disease Maps</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">2015-2035</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This article summarizes our research on estimation of age-specific and age-adjusted mortality rates for chronic obstructive pulmonary disease (COPD) for white males. Our objectives are more precise and informative displays (than previously available) of geographic variation of the age-specific mortality rates for COPD, and investigation of the relationships between the geographic variation in mortality rates and the corresponding variation in selected covariates. For a given age class, our estimates are displayed in a choropleth map of mean rates. We develop a variation map that identifies the geographical areas where inferences are reliable. Here, the variation is measured by considering a set of maps produced using samples from the posterior distribution of the population mortality rates. Finally, we describe the spatial patterns in the age-specific maps and relate these to patterns in potential explanatory covariates such as smoking rate, annual rainfall, population density, elevation, and measures of air quality.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">N. Raghavan</style></author><author><style face="normal" font="default" size="100%">R. Bell</style></author><author><style face="normal" font="default" size="100%">M. Schonlau</style></author><author><style face="normal" font="default" size="100%">D. Pregibon</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Defection detection: Using online activity profiles to predict ISP customer vulnerability</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">506?515</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rouphail, N.</style></author><author><style face="normal" font="default" size="100%">Park, B.</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct Signal Timing Optimization: Strategy Development and Results</style></title><secondary-title><style face="normal" font="default" size="100%">XI Pan American Conference in Traffic and Transportation Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2000</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">19-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">M. Ghandehari</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of water flow through cracked concrete under load</style></title><secondary-title><style face="normal" font="default" size="100%">ACI Materials Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">97</style></volume><pages><style face="normal" font="default" size="100%">567?575</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This research studied the relationship between cracking and water permeability of normal-strength concrete under load and compared the experimental results with theoretical models. A feedback-controlled wedge splitting test was used to generate width-controlled cracks. Speckle interferometry was used to record the cracking history. Water permeability of the loaded specimens was evaluated by a low-pressure water permeability test at the designed crack mouth opening displacements (CMODs). Water permeability results were compared with those previously obtained for unloaded specimens for which cracks were induced by a feedback-controlled splitting tension test. The experimental results indicate that water permeability of cracked material significantly increases with increasing crack width. The flow for the same cracking level is repeatable regardless of the procedure used for inducing the cracks. No direct relationship between water flow and crack length was observed, whereas clear relationships existed between CMOD or crack area and flow characteristics. Experimentally measured flow was compared with theoretical models of flow through cracked rocks with parallel walls and a correction factor accounting for the tortuosity of the crack was determined. Calculated flow through cracks induced by a wedge-splitting test provided an acceptable approximation of the measured flow.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">J.D. Picka</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">S.S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Experimental and statistical study of chloride permeability of cracked high strength concrete</style></title><secondary-title><style face="normal" font="default" size="100%">ASTM Cement, Concrete and Aggregates</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">000-000</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Within any cast cylinder of concrete, the coarse aggregate will tend to be inhomogeneously distributed. This variability may arise as a result of segregation caused by gravity or as a result of the wall effect that is caused by the inability of the aggregate to penetrate the walls of the mold. Using methods from image analysis, stereology, and statistics, local estimates of aggregate inhomogeniety are defined that quantify phenomena that have been qualitatively described in the past. These methods involve modification of the two-dimensional images to prepare them for analysis, as well as simple diagnostic statistics for determining the presence of a wall effect. While the techniques presented herein are developed specifically for cast cylinders, they can be generalized to other cast or cored concrete specimens.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">S.S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">B.E. Ankenman</style></author><author><style face="normal" font="default" size="100%">J.D. Picka</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impact of the interfacial transition zone on the chloride permeability of concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 12th Engrg. Mechanics Conf</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">1134-1137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. G. Eick</style></author><author><style face="normal" font="default" size="100%">T.L. Graves</style></author><author><style face="normal" font="default" size="100%">J. S. Marron</style></author><author><style face="normal" font="default" size="100%">H. Siy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting fault incidence using software change history</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transportation Software Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">aging</style></keyword><keyword><style  face="normal" font="default" size="100%">change history</style></keyword><keyword><style  face="normal" font="default" size="100%">degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">management of change</style></keyword><keyword><style  face="normal" font="default" size="100%">software fault tolerance</style></keyword><keyword><style  face="normal" font="default" size="100%">software maintenance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">653?661</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper is an attempt to understand the processes by which software ages. We define code to be aged or decayed if its structure makes it unnecessarily difficult to understand or change and we measure the extent of decay by counting the number of faults in code in a period of time. Using change management data from a very large, long-lived software system, we explore the extent to which measurements from the change history are successful in predicting the distribution over modules of these incidences of faults. In general, process measures based on the change history are more useful in predicting fault rates than product metrics of the code: For instance, the number of times code has been changed is a better indication of how many faults it will contain than is its length. We also compare the fault rates of code of various ages, finding that if a module is, on the average, a year older than an otherwise similar module, the older module will have roughly a third fewer faults. Our most successful model measures the fault potential of a module as the sum of contributions from all of the times the module has been changed, with large, recent changes receiving the most weight&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S.S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author><author><style face="normal" font="default" size="100%">J.D. Picka</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative description of coarse aggregate volume fraction gradients</style></title><secondary-title><style face="normal" font="default" size="100%">Cement Concrete and Aggregates</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">151-159</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Within any cast cylinder of concrete, the coarse aggregate will tend to be inhomogeneously distributed. This variability may arise as a result of segregation caused by gravity or as a result of the wall effect that is caused by the inability of the aggregate to penetrate the walls of the mold. Using methods from image analysis, stereology, and statistics, local estimates of aggregate inhomogeniety are defined that quantify phenomena that have been qualitatively described in the past. These methods involve modification of the two-dimensional images to prepare them for analysis, as well as simple diagnostic statistics for determining the presence of a wall effect. While the techniques presented herein are developed specifically for cast cylinders, they can be generalized to other cast or cored concrete specimens.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Speckman, Paul</style></author><author><style face="normal" font="default" size="100%">Tsutakawa, R. K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Random effects in generalized linear mixed models (GLMMs)</style></title><secondary-title><style face="normal" font="default" size="100%">Generalized Linear Models: A Bayesian Perspective</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Marcel dekker, Inc.</style></publisher><pages><style face="normal" font="default" size="100%">23-40</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">RICHARD L. SMITH</style></author><author><style face="normal" font="default" size="100%">J.M. Davis</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Speckman, Paul</style></author><author><style face="normal" font="default" size="100%">P. Styer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regression models for air pollution and daily mortality: analysis of data from Birmingham, Alabama</style></title><secondary-title><style face="normal" font="default" size="100%">Environmetrics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Air Pollutants/adverse effects</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollutants/analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution/adverse effects</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution/analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution/statistics &amp; numerical data</style></keyword><keyword><style  face="normal" font="default" size="100%">Alabama/epidemiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Mortality</style></keyword><keyword><style  face="normal" font="default" size="100%">Poisson Distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">Regression Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">719-743</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Several recent studies have reported associations between common levels of particulate air pollution and small increases in daily mortality. This study examined whether a similar association could be found in the southern United States, with different weather patterns than the previous studies, and examined the sensitivity of the results to different methods of analysis and covariate control. Data were available in Birmingham, Alabama, from August 1985 through 1988. Regression analyses controlled for weather, time trends, day of the week, and year of study and removed any long-term patterns (such as seasonal and monthly fluctuations) from the data by trigonometric filtering. A significant association was found between inhalable particles and daily mortality in Poisson regression analysis (relative risk = 1.11, 95% confidence interval 1.02-1.20). The relative risk was estimated for a 100-micrograms/m3 increase in inhalable particles. Results were unchanged when least squares regression was used, when robust regression was used, and under an alternative filtering scheme. Diagnostic plots showed that the filtering successfully removed long wavelength patterns from the data. The generalized additive model, which models the expected number of deaths as nonparametric smoothed functions of the covariates, was then used to ensure adequate control for any nonlinearities in the weather dependence. Essentially identical results for inhalable particles were seen, with no evidence of a threshold down to the lowest observed exposure levels. The association also was unchanged when all days with particulate air pollution levels in excess of the National Ambient Air Quality Standards were deleted. The magnitude of the effect is consistent with recent estimates from Philadelphia, Steubenville, Detroit, Minneapolis, St. Louis, and Utah Valley.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S.S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">J.D. Picka</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">B.E. Ankenman</style></author><author><style face="normal" font="default" size="100%">P. Styer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical studies of the conductivity of concrete using ASTM C1202?94</style></title><secondary-title><style face="normal" font="default" size="100%">Concrete Science and Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">97-105</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Park, B.</style></author><author><style face="normal" font="default" size="100%">N. M. Rouphail</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traffic Signal Offset Optimization Using Microscopic Simulation Program with Stochastic Process Model</style></title><secondary-title><style face="normal" font="default" size="100%">American Society of Civil Engineers</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Woodard, R.</style></author><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author><author><style face="normal" font="default" size="100%">Sheriff, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A bivariate Bayes method for improving the estimates of mortality rates with a twofold conditional autoregressive model</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural Biological and Environmental Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Missouri Turkey Hunting Survey (MTHS) is a post-season mail survey conducted by the Missouri Department of Conservation to monitor and aid in the regulation of the turkey hunting season. Questionnaires are distributed after the hunting season to a simple random sample of persons who purchased permits to hunt wild turkey during the spring season. For the 1996 turkey hunting season 95,801 persons purchased hunting permits. From these individuals a simple random sample of 6,999 hunters were selected for the survey and 5,005 of these responded. The MTHS 1 Roger Woodard (E-mail: woodard@stat.missouri.edu) is a Ph.D student and Dongchu Sun (E-mail: dsun@stat.missouri.edu) is Associate Professor of Statistics, Department of Statistics, University of Missouri, Columbia, MO 65211. Zhuoqiong He (E-mail: HEZ@mail.conservation.state.mo.us) is a biometrician and Steven L. Sheri (E-mail: SHERIS@mail.conservation.state.mo.us) is a wildlife biometrics superv&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abt, Markus</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design and Analysis for Modeling and Predicting Spatial Contamination</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical Geology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">best linear unbiased prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">dioxin contamination</style></keyword><keyword><style  face="normal" font="default" size="100%">Gaussian stochastic process</style></keyword><keyword><style  face="normal" font="default" size="100%">lognormal kriging</style></keyword><keyword><style  face="normal" font="default" size="100%">ordinary kriging</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial statistics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1023/A%3A1007504329298</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers-Plenum Publishers</style></publisher><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">1-22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Sampling and prediction strategies relevant at the planning stage of the cleanup of environmental hazards are discussed. Sampling designs and models are compared using an extensive set of data on dioxin contamination at Piazza Road, Missouri. To meet the assumptions of the statistical model, such data are often transformed by taking logarithms. Predicted values may be required on the untransformed scale, however, and several predictors are also compared. Fairly small designs turn out to be sufficient for model fitting and for predicting. For fitting, taking replicates ensures a positive measurement error variance and smooths the predictor. This is strongly advised for standard predictors. Alternatively, we propose a predictor linear in the untransformed data, with coefficients derived from a model fitted to the logarithms of the data. It performs well on the Piazza Road data, even with no replication.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr.</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of cracking on water and chloride permeability of concrete</style></title><secondary-title><style face="normal" font="default" size="100%">ACSE Journal of Materials in Civil Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">181?187</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The goal of this research was to study the relationship between cracking and concrete permeability and to support accounting for permeability and cracking resistance to other factors besides strength, as criteria to be considered in mix design to achieve a durable concrete. The effect of material composition [normal-strength concrete (NSC) and high-strength concrete (HSC) with two different mix designs] and crack width (ranging from 50 to 400 ?m) on water and chloride permeability were examined. Cracks of designed widths were induced in the concrete specimens using a feedback-controlled splitting tensile test. Chloride permeability of the cracked samples was evaluated using a rapid chloride permeability test and the water permeability of cracked concrete was then evaluated by a low-pressure water permeability test. Uncracked HSC was less water permeable than NSC, as expected, but cracking changed the material behavior in terms of permeability. Both NSC and HSC were affected by cracking, and the water permeability of cracked samples increased with increasing crack width. Among the tested materials, only HSC with a very low water-to-cement ratio chloride permeability was sensitive with respect to cracking. Results indicate that the water permeability is significantly more sensitive than the chloride permeability with respect to the crack widths used in this study.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of microcracking on durability of high strength concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Record</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">1668</style></volume><pages><style face="normal" font="default" size="100%">86-90</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The relationship between cracking and chloride and water permeability of high-strength concrete (HSC) was studied. Two different mix designs were used: HSC_1 (w/b = 0.31) and HSC_2 (w/b = 0.25). The effects of crack width and sample thickness on permeability were examined. Cracks of designed widths were induced in the concrete specimens using the feedback-controlled splitting tensile test. Chloride permeability of the cracked samples was evaluated by using a rapid chloride permeability test. The water permeability of cracked concrete was then evaluated by a low-pressure water permeability test. Among the materials tested, only high-strength concrete with a very low water-to-cement ratio conductivity is sensitive with respect to cracking. The water permeability of cracked HSC significantly increases with increasing crack width. Among the parameters considered, crack parameters significantly affect water permeability, and there is little thickness effect. The results indicate that the water permeability is significantly more sensitive than conductivity with respect to the crack width used.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">P. Metaxatos</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of Demand due to Welfare Reform</style></title><secondary-title><style face="normal" font="default" size="100%">In Papers in Regional Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">195 – 211</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Huang, Li-Shan</style></author><author><style face="normal" font="default" size="100%">RICHARD L. SMITH</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Meteorologically-dependent trends in urban ozone</style></title><secondary-title><style face="normal" font="default" size="100%">Environmetrics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ANOVA</style></keyword><keyword><style  face="normal" font="default" size="100%">empirical Bayes</style></keyword><keyword><style  face="normal" font="default" size="100%">regression tree</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">103–118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ozone concentrations are affected by precursor emissions and by meteorological conditions. As part of a broad study to assess the effects of standards imposed by the U.S. Environmental Protection Agency (EPA), it is of interest to analyze trends in ozone after adjusting for meteorological influences. Previous papers have studied this problem for ozone data from Chicago, using a variety of regression techniques. This paper presents a different approach, in which the meteorological influence is treated nonlinearly through a regression tree. A particular advantage of this approach is that it allows us to consider different trends within the clusters produced by the regression tree analysis. The variability of trend estimates between clusters is reduced by applying an empirical Bayes adjustment. The results confirm the findings of previous authors that there is an overall downward trend in Chicago ozone values, but they also go beyond previous analyses by showing that the trend is stronger at higher levels of ozone. Copyright © 1999 John Wiley &amp;amp; Sons, Ltd.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Permeability of cracked concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Materials and Structures</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">370-376</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The goal of the research presented here was to study the relationship between cracking and water permeability. A feedback-controlled test was used to generate width-controlled cracks. Water permeability was evaluated by a low-pressure water permeability test. The factors chosen for the experimental design were material type (paste, mortar, normal and high strength concrete), thickness of the sample and average width of the induced cracks (ranging from 50 to 350 micrometers). The water permeability test results indicated that the relationships between permeability and material type differ for uncracked and cracked material, and that there was little thickness effect. Permeability of uncracked material decreased from paste, mortar, normal strength concrete (NSC) to high strength concrete (HSC). Water permeability of cracked material significantly increased with increasing crack width. For cracks above 100 microns, NSC showed the highest permeability coefficient, where as mortar showed the lowest one.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">P. C. Aïtcin</style></author><author><style face="normal" font="default" size="100%">Y. Delagrave</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Permeability of cracked high strength concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Symposium on High Performance and Reactive Powder Concretes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><pages><style face="normal" font="default" size="100%">211-219</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The goal of the research presented here was to study the relationship between cracking and water permeability. A feedback-controlled test was used to generate width-controlled cracks. Water permeability was evaluated by a low-pressure water permeability test. The factors chosen for the experimental design were material type (paste, mortar, normal and high strength concrete), thickness of the sample and average width of the induced cracks (ranging from 50 to 350 micrometers). The water permeability test results indicated that the relationships between permeability and material type differ for uncracked and cracked material, and that there was little thickness effect. Permeability of uncracked material decreased from paste, mortar, normal strength concrete (NSC) to high strength concrete (HSC). Water permeability of cracked material significantly increased with increasing crack width. For cracks above 100 microns, NSC showed the highest permeability coefficient, where as mortar showed the lowest one.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Tsuakawa, R. K.</style></author><author><style face="normal" font="default" size="100%">Speckman, Paul</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Posterior distribution of hierarchical models using CAR(1) distributions</style></title><secondary-title><style face="normal" font="default" size="100%">Biometrika</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Gibbs sampling</style></keyword><keyword><style  face="normal" font="default" size="100%">Linear mixed model</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate normal</style></keyword><keyword><style  face="normal" font="default" size="100%">Partially informative normal distribution</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">86</style></volume><pages><style face="normal" font="default" size="100%">341-350</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We examine properties of the conditional autoregressive model, or CAR(1) model, which is commonly used to represent regional effects in Bayesian analyses of mortality rates. We consider a Bayesian hierarchical linear mixed model where the fixed effects have a vague prior such as a constant prior and the random effect follows a class of CAR(1) models including those whose joint prior distribution of the regional effects is improper. We give sufficient conditions for the existence of the posterior distribution of the fixed and random effects and variance components. We then prove the necessity of the conditions and give a one-way analysis of variance example where the posterior may or may not exist. Finally, we extend the result to the generalised linear mixed model, which includes as a special case the Poisson log-linear model commonly used in disease&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">P. Thakuriah</style></author><author><style face="normal" font="default" size="100%">A. Sen</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">R. Emmerink</style></author><author><style face="normal" font="default" size="100%">P. Nijkamp</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Probe-based surveillance for travel time information in ITS</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Ashgate Publishing Ltd</style></publisher><pages><style face="normal" font="default" size="100%">393-425</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">17</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. Sen</style></author><author><style face="normal" font="default" size="100%">P. Thakuriah</style></author><author><style face="normal" font="default" size="100%">X. Zhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variances of link travel time estimates: Implications for optimal routes</style></title><secondary-title><style face="normal" font="default" size="100%">International Transactions in Operational Research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced Traveler Information System</style></keyword><keyword><style  face="normal" font="default" size="100%">Covariance of travel times</style></keyword><keyword><style  face="normal" font="default" size="100%">Dependence in travel time observations</style></keyword><keyword><style  face="normal" font="default" size="100%">Intelligent Transportation System</style></keyword><keyword><style  face="normal" font="default" size="100%">Probe vehicles</style></keyword><keyword><style  face="normal" font="default" size="100%">Variance of travel time estimates</style></keyword><keyword><style  face="normal" font="default" size="100%">Vehicle simulation model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">January</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">75-87</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we explore the consequences of using link travel time estimates with high variance to compute the minimum travel time route between an origin and destination pair. Because of platoon formation or for other reasons, vehicles on a link separated by small headways tend to have similar travel times. In other words, the covariance of link travel times of distinct vehicles which are close together may not be zero. It follows that the variance of the mean of travel times obtained from a sample of n vehicles on a same link over small time intervals is of the form a+b/n where a and b would usually be positive. This result has an important implication for the quality of road network travel time information given by Intelligent Transportation Systems (ITS)?that the variance of the estimate of mean travel time does not go to zero with increasing n. Thus the quality of information disseminated by ITS is not necessarily improved by increasing the market penetration of vehicles monitoring the system with the necessary equipment (termed probe vehicles). Estimates of a and b for a set of links are presented in the paper and consequences for probe-based ITS are explored by means of a simulation of such a system which is operational on an actual network.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Metaxatos, Paul</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Thakuriah, Vonu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Welfare reform and spatial matching between clients and jobs</style></title><secondary-title><style face="normal" font="default" size="100%">Papers in Regional Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">C13</style></keyword><keyword><style  face="normal" font="default" size="100%">C51</style></keyword><keyword><style  face="normal" font="default" size="100%">C52</style></keyword><keyword><style  face="normal" font="default" size="100%">entry-level job openings.</style></keyword><keyword><style  face="normal" font="default" size="100%">I31</style></keyword><keyword><style  face="normal" font="default" size="100%">J23</style></keyword><keyword><style  face="normal" font="default" size="100%">JEL classification:C12</style></keyword><keyword><style  face="normal" font="default" size="100%">Key words:Welfare to work</style></keyword><keyword><style  face="normal" font="default" size="100%">R12</style></keyword><keyword><style  face="normal" font="default" size="100%">R41</style></keyword><keyword><style  face="normal" font="default" size="100%">R53</style></keyword><keyword><style  face="normal" font="default" size="100%">targeted service</style></keyword><keyword><style  face="normal" font="default" size="100%">travel demand</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s101100050021</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">195-211</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The recent Welfare Reform Act requires several categories of public assistance recipients to transition to the work force. In most metropolitan areas public assistance clients reside great distances from areas of entry-level jobs. Any program designed to provide access to these jobs, for those previously on public aid, needs relevant transportation services when the job search process begins. Therefore it is essential that the latent demand for commuting among public aid clients be assessed in developing public transportation services. The location of entry-level jobs must also be known or, as in this article, estimated using numerous data sources. This article reports on such a demand estimation effort, focusing primarily on the use of Regional Science methods.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xie, Minge</style></author><author><style face="normal" font="default" size="100%">Simpson, Douglas</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Nychka, Douglas</style></author><author><style face="normal" font="default" size="100%">Piegorsch, Walter W.</style></author><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Categorical Exposure-Response Regression Analysis of Toxicology Experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Environmental Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2226-2_7</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><volume><style face="normal" font="default" size="100%">132</style></volume><pages><style face="normal" font="default" size="100%">121-141</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-98478-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In the mid-1980s, an accident at the Union Carbide pesticides plant in Bhopal, India released the toxic gas methylisocyanate (MIC) in that densely populated region, killing more than 4000 people and injuring 500,000 others. Even today, many people in Bhopal are affected by illnesses related to that earlier exposure. This notorious industrial disaster not only forced scientists to pay greater attention to identifying and handling of hazardous chemicals but also prompted greater awareness of those common industrial products that contain hazard pollutants.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aslett, Robert</style></author><author><style face="normal" font="default" size="100%">Buck, Robert J.</style></author><author><style face="normal" font="default" size="100%">Duvall, Steven G.</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Circuit optimization via sequential computer experiments: design of an output buffer</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Royal Statistical Society: Series C</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Circuit simulator</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer code</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer model</style></keyword><keyword><style  face="normal" font="default" size="100%">Engineering design</style></keyword><keyword><style  face="normal" font="default" size="100%">Parameter design</style></keyword><keyword><style  face="normal" font="default" size="100%">Stochastic process</style></keyword><keyword><style  face="normal" font="default" size="100%">Visualization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">31-48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In electrical engineering, circuit designs are now often optimized via circuit simulation computer models. Typically, many response variables characterize the circuit’s performance. Each response is a function of many input variables, including factors that can be set in the engineering design and noise factors representing manufacturing conditions. We describe a modelling approach which is appropriate for the simulator’s deterministic input–output relationships. Non-linearities and interactions are identified without explicit assumptions about the functional form. These models lead to predictors to guide the reduction of the ranges of the designable factors in a sequence of experiments. Ultimately, the predictors are used to optimize the engineering design. We also show how a visualization of the fitted relationships facilitates an understanding of the engineering trade-offs between responses. The example used to demonstrate these methods, the design of a buffer circuit, has multiple targets for the responses, representing different trade-offs between the key performance measures.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author><author><style face="normal" font="default" size="100%">Condie, Helen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of static travel times in a dynamic route guidance system—II</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical and Computer Modelling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced Traveler Information Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic Route Guidance</style></keyword><keyword><style  face="normal" font="default" size="100%">Link travel times</style></keyword><keyword><style  face="normal" font="default" size="100%">Static estimates</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">67–85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In an earlier paper a method for computing static profiles of link travel times was given. In this paper, the centrality of such profiles for ATIS is examined and the methods given in the earlier paper are applied to actual data. Except for a minor, easily correctable problem, the methods are shown to work very well under real-life conditions.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Schonlau</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author><author><style face="normal" font="default" size="100%">Jones, Donald R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global versus Local Search in Constrained Optimization of Computer Models</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes-Monograph Series</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian global optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer code</style></keyword><keyword><style  face="normal" font="default" size="100%">sequential design</style></keyword><keyword><style  face="normal" font="default" size="100%">Stochastic process</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">11-25</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Engineering systems are now frequently optimized via computer models. The input-output relationships in these models are often highly nonlinear deterministic functions that are expensive to compute. Thus, when searching for the global optimum, it is desirable to minimize the number of function evaluations. Bayesian global optimization methods are well-suited to this task because they make use of all previous evaluations in selecting the next search point. A statistical model is fit to the sampled points which allows predictions to be made elsewhere, along with a measure of possible prediction error (uncertainty). The next point is chosen to maximize a criterion that balances searching where the predicted value of the function is good (local search) with searching where the uncertainty of prediction is large (global search). We extend this methodology in several ways. First, we introduce a parameter that controls the local-global balance. Secondly, we propose a method for dealing with nonlinear inequality constraints from additional response variables. Lastly, we adapt the sequential algorithm to proceed in stages rather than one point at a time. The extensions are illustrated using a shape optimization problem from the automotive industry.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Williams, Valerie</style></author><author><style face="normal" font="default" size="100%">Billeaud, Kathleen</style></author><author><style face="normal" font="default" size="100%">Davis, Lori A.</style></author><author><style face="normal" font="default" size="100%">Thissen, David</style></author><author><style face="normal" font="default" size="100%">Sanford, Eleanor E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Projecting to the NAEP Scale: Results from the North Carolina End-of-Grade Testing Program</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Educational Measurement</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">277-296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Data from the North Carolina End-of-Grade test of eighth-grade mathematics are used to estimate the achievement results on the scale of the National Assessment of Educational Progress (NAEP) Trial State Assessment. Linear regression models are used to develop projection equations to predict state NAEP results in the future, and the results of such predictions are compared with those obtained in the 1996 administration of NAEP. Standard errors of the parameter estimates are obtained using a bootstrap resampling technique.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boyce, D. E.</style></author><author><style face="normal" font="default" size="100%">Lee, D.-H.</style></author><author><style face="normal" font="default" size="100%">Janson, B.N.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Beckmann, Martin J.</style></author><author><style face="normal" font="default" size="100%">Johannsson, Börje</style></author><author><style face="normal" font="default" size="100%">Snickars, Folke</style></author><author><style face="normal" font="default" size="100%">Thord, Roland</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Roadway Incident Analysis with a Dynamic User-Optimal Route Choice Model</style></title><secondary-title><style face="normal" font="default" size="100%">Knowledge and Networks in a Dynamic Economy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-60318-1_21</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">371-390</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-64350-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The transportation system conveys interdependencies. When analysing the costs and benefits of transport investment projects, it is therefore necessary to address the question of linkages among projects. Such linkages can occur in terms of economies of scale in arising from the combination of projects during the construction phase. Intelligent Transportation Systems (ITS), also known as Intelligent Vehicle Highway Systems (IVHS), are applying advanced technologies (such as navigation, automobile, computer science, telecommunication, electronic engineering, automatic information collection and processing) in an effort to bring revolutionary improvements in traffic safety, network capacity utilization, vehicle emission reductions, travel time and fuel consumption savings, etc. Within the framework of ITS, Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) both aim to manage and predict traffic congestion and provide historical and real time network-wide traffic information to support drivers’ route choice decisions. To enable ATMS/ATIS to achieve the above described goals, traffic flow prediction models are needed for system operation and evaluation. Linkages may also arise in supply through interaction among network components, or among the producers of transportation services. Linkages may also emerge in demand through the creation of new opportunities for interaction.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Metaxatos, Paul</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transportation Planning Process for Linking Welfare Recipients to Jobs</style></title><secondary-title><style face="normal" font="default" size="100%">In Transportation Research Record</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">1626</style></volume><pages><style face="normal" font="default" size="100%">149 - 158</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of Parameters  in Mechanistic Models:  A Case Study of PCB Fate and Transport in Surface Waters</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">97</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Sen</style></author><author><style face="normal" font="default" size="100%">P. Thakuriah</style></author><author><style face="normal" font="default" size="100%">X. Zhu</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Frequency of probe vehicle reports and variances of link travel time estimates</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Transportation Engineering, ASCE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">123</style></volume><pages><style face="normal" font="default" size="100%">290?297</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;An important design issue relating to probe-based Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems is the sample size of probes (or the number of link traversals by probe vehicles) per unit time used in order to obtain reliable network information in terms of link travel time estimates. The variance of the mean of travel times obtained from n probes for the same link over a fixed time period may be shown to be of the form a+b/n where a and b are link-specific parameters. Using probe travel time data from a set of signalized arterials, it is shown that a is positive for well-traveled signalized links. This implies that the variance does not go to zero with increasing n. Consequences of this fact for probe-based systems are explored. While the results presented are for a specific set of links, we argue that because of the nature of the underlying travel time process, the broad conclusions would hold for most well-traveled links with signal control.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author><author><style face="normal" font="default" size="100%">T. Styer</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Influence of microstructure and fracture on the transport properties in cement-based materials</style></title><secondary-title><style face="normal" font="default" size="100%">Brittle Matrix Composites - International Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">199-220</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">K. Wang</style></author><author><style face="normal" font="default" size="100%">D.C. Jansen</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Permeability study of cracked concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Cement Concrete Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">381-393</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cracks in concrete generally interconnect flow paths and increase concrete permeability. The increase in concrete permeability due to the progression of cracks allows more water or aggressive chemical ions to penetrate into the concrete, facilitating deterioration. The present work studies the relationship between crack characteristics and concrete permeability. In this study, feedback controlled splitting tests are introduced to generate crack width-controlled concrete specimens. Sequential crack patterns with different crack widths are viewed under a microscope. The permeability of cracked concrete is evaluated by water permeability tests. The preliminary results indicate that crack openings generally accelerate water flow rate in concrete. When a specimen is loaded to have a crack opening displacement smaller than 50 microns prior to unloading, the crack opening has little effect on concrete permeability. When the crack opening displacement increases from 50 microns to about 200 microns, concrete permeability increases rapidly. After the crack opening displacement reaches 200 microns, the rate of water permeability increases steadily. The present research may provide insight into developing design criteria for a durable concrete and in predicting service life of a concrete structure.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nobile, Agostino</style></author><author><style face="normal" font="default" size="100%">Bhat, Chandra R.</style></author><author><style face="normal" font="default" size="100%">Pas, Eric I.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gatsonis, Constantine</style></author><author><style face="normal" font="default" size="100%">Hodges, JamesS.</style></author><author><style face="normal" font="default" size="100%">Kass, RobertE.</style></author><author><style face="normal" font="default" size="100%">McCulloch, Robert</style></author><author><style face="normal" font="default" size="100%">Rossi, Peter</style></author><author><style face="normal" font="default" size="100%">Singpurwalla, NozerD.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Random-Effects Multinomial Probit Model of Car Ownership Choice</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Bayesian Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">car ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">longitudinal data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2290-3_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">121</style></volume><pages><style face="normal" font="default" size="100%">419-434</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-94990-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The number of cars in a household has an important effect on its travel behavior (e.g., choice of number of trips, mode to work and non-work destinations), hence car ownership modeling is an essential component of any travel demand forecasting effort. In this paper we report on a random effects multinomial probit model of car ownership level, estimated using longitudinal data collected in the Netherlands. A Bayesian approach is taken and the model is estimated by means of a modification of the Gibbs sampling with data augmentation algorithm considered by McCulloch and Rossi (1994). The modification consists in performing, after each Gibbs sampling cycle, a Metropolis step along a direction of constant likelihood. An examination of the simulation output illustrates the improved performance of the resulting sampler.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xie, Minge</style></author><author><style face="normal" font="default" size="100%">Simpson, Douglas G</style></author><author><style face="normal" font="default" size="100%">Carroll, Raymond J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gregoire, Timothy G.</style></author><author><style face="normal" font="default" size="100%">Brillinger, David R.</style></author><author><style face="normal" font="default" size="100%">Diggle, PeterJ.</style></author><author><style face="normal" font="default" size="100%">Russek-Cohen, Estelle</style></author><author><style face="normal" font="default" size="100%">Warren, William G.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, Russell D.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Scaled Link Functions for Heterogeneous Ordinal Response Data*</style></title><secondary-title><style face="normal" font="default" size="100%">Modelling Longitudinal and Spatially Correlated Data</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aggregated observations</style></keyword><keyword><style  face="normal" font="default" size="100%">Generalized likelihood inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Marginal modeling approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Ordinal regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-0699-6_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">23-36</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-98216-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper describes a class ordinal regression models in which the link function has scale parameters that may be estimated along with the regression parameters. One motivation is to provide a plausible model for group level categorical responses. In this case a natural class of scaled link functions is obtained by treating the group level responses as threshold averages of possible correlated latent individual level variables. We find scaled link functions also arise naturally in other circumstances. Our methodology is illustrated through environmental risk assessment data where (correlated) individual level responses and group level responses are mixed.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bloomfield, Peter</style></author><author><style face="normal" font="default" size="100%">Royle, Andy</style></author><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Yang, Qing</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accounting for  Meteorological Effects in Measuring Urban Ozone Levels and Trends</style></title><secondary-title><style face="normal" font="default" size="100%">Atmospheric Environment</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">median polish</style></keyword><keyword><style  face="normal" font="default" size="100%">meteorological adjustment</style></keyword><keyword><style  face="normal" font="default" size="100%">nonlinear regression</style></keyword><keyword><style  face="normal" font="default" size="100%">nonparametric regression</style></keyword><keyword><style  face="normal" font="default" size="100%">Ozone concentration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">3067–3077</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Observed ozone concentrations are valuable indicators of possible health and environmental impacts. However, they are also used to monitor changes and trends in the sources of ozone and of its precursors, and for this purpose the influence of meteorological variables is a confounding factor. This paper examines ozone concentrations and meteorology in the Chicago area. The data are described using least absolute deviations and local regression. The key relationships observed in these analyses are then used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology, and to ‘adjust’ observed ozone concentrations for anomalous weather conditions.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">17</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Model for Fate and  Transport of Polychlorinated Biphenyl in Upper Hudson River</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Environmental Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">122</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Model for Fate and Transport of Polychlorinated Biphenyl in Upper Hudson River</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Environmental Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Hudson River</style></keyword><keyword><style  face="normal" font="default" size="100%">PCB</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation models</style></keyword><keyword><style  face="normal" font="default" size="100%">transport phenomena</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May 1996</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">341-349</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modelers of contaminant fate and transport in surface waters typically rely on literature values when selecting parameter values for mechanistic models. While the expert judgment with which these selections are made is valuable, the information contained in contaminant concentration measurements should not be ignored. In this full-scale Bayesian analysis of polychlorinated biphenyl (PCB) contamination in the upper Hudson River, these two sources of information are combined using Bayes’ theorem. A simulation model for the fate and transport of the PCBs in the upper Hudson River forms the basis of the likelihood function while the prior density is developed from literature values. The method provides estimates for the anaerobic biodegradation half-life, aerobic biodegradation plus volatilization half-life, contaminated sediment depth, and resuspension velocity of 4,400 d, 3.2 d, 0.32 m, and 0.02 m/yr, respectively. These are significantly different than values obtained with more traditional methods, and are shown to produce better predictions than those methods when used in a cross-validation study.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Simpson, Douglas G</style></author><author><style face="normal" font="default" size="100%">Carroll, Raymond</style></author><author><style face="normal" font="default" size="100%">Xie, Minge</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interval Censoring And Marginal Analysis In Ordinal Regression</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural Biological and Environmental Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">categorical data</style></keyword><keyword><style  face="normal" font="default" size="100%">categorical response</style></keyword><keyword><style  face="normal" font="default" size="100%">environmental statistics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">4</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper develops methodology for regression analysis of ordinal response data subject to interval censoring. This work is motivated by the need to analyze data from multiple studies in toxicological risk assessment. Responses are scored on an ordinal severity scale, but not all responses can be scored completely. For instance, in a mortality study, information on nonfatal but adverse outcomes may be missing. In order to address possible within–study correlations we develop a generalized estimating approach to the problem, with appropriate adjustments to uncertainty statements. We develop expressions relating parameters of the implied marginal model to the parameters of a conditional model with random effects, and, in a special case, we note an interesting equivalence between conditional and marginal modeling of ordinal responses. We illustrate the methodology in an analysis of a toxicological data-base.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Christopher, Ed J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non - response and Urban Travel Models</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Record</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">1551</style></volume><pages><style face="normal" font="default" size="100%">82 - 87</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gao, Feng</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Welch, William</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting ozone levels and trends with semiparametric modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural, Biological, and Environmental Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">404-425</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">404</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quality of Information given by Advanced Traveler Information Systems</style></title><secondary-title><style face="normal" font="default" size="100%">In Transporta tion Research Part C: Emerging Technologies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">249 - 266</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Styer</style></author><author><style face="normal" font="default" size="100%">McMillan, N</style></author><author><style face="normal" font="default" size="100%">Gao, F</style></author><author><style face="normal" font="default" size="100%">Davis, J</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of outdoor airborne particulate matter on daily death count</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental Health Perspectives</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">103</style></volume><pages><style face="normal" font="default" size="100%">490–497</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To investigate the possible relationship between airborne particulate matter and mortality, we developed regression models of daily mortality counts using meteorological covariates and measures of outdoor PM10. Our analyses included data from Cook County, Illinois, and Salt Lake County, Utah. We found no evidence that particulate matter &amp;lt; or = 10 microns (PM10) contributes to excess mortality in Salt Lake County, Utah. In Cook County, Illinois, we found evidence of a positive PM10 effect in spring and autumn, but not in winter and summer. We conclude that the reported effects of particulates on mortality are unconfirmed.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of Static Travel Times in a Dynamic Route Guidance System</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical and Computer Modelling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced Travel Information System</style></keyword><keyword><style  face="normal" font="default" size="100%">Autonomous route guidance</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic Route Guidance</style></keyword><keyword><style  face="normal" font="default" size="100%">Link travel time estimate</style></keyword><keyword><style  face="normal" font="default" size="100%">Link Travel Time Process</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">83–101</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In an Advanced Traveler Information System where route guidance is provided, a driver chooses a route before he/she actually traverses the links in the route. For such systems, link travel times need to be forecasted. However, information on several thousand links would take a fair amount of time to be conveyed to the driver, and very few drivers would be willing to wait very long to get route information, In the ADVANCE demonstration, to be implemented in suburban Chicago, the in-vehicle unit in each participating vehicle will be provided with the capability of accessing default travel time information, which will offer the vehicle with an autonomous navigation capability. The default estimates will be overwritten by dynamic up-to-the-minute forecasts if such forecasts are different from the default estimates. This paper describes the approach used to compute default travel times estimates.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shaffer, Juliet Popper</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Hypothesis Testing: A Review</style></title><secondary-title><style face="normal" font="default" size="100%">Annual Review of Psychology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">561-584</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Smith, R.L.</style></author><author><style face="normal" font="default" size="100%">Shively, Thomas S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Point process approach to modeling trends in tropospheric ozone based on exceedances of a high threshold</style></title><secondary-title><style face="normal" font="default" size="100%">Atmospheric Environment</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">3489–3499</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">3489</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chapman, W.L.</style></author><author><style face="normal" font="default" size="100%">Welch, W.</style></author><author><style face="normal" font="default" size="100%">Bowman, K.P.</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Walsh, J.E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Arctic sea ice variability: Model sensitivities and a multidecadal simulation</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Geophysical Research: Oceans</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arctic region</style></keyword><keyword><style  face="normal" font="default" size="100%">Climate and interannual variability</style></keyword><keyword><style  face="normal" font="default" size="100%">Climate and interannual variability Ice mechanics and air/sea/ice exchange processes</style></keyword><keyword><style  face="normal" font="default" size="100%">Ice mechanics and air/sea/ice exchange processes</style></keyword><keyword><style  face="normal" font="default" size="100%">Information Related to Geographic Region: Arctic region</style></keyword><keyword><style  face="normal" font="default" size="100%">Numerical modeling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><volume><style face="normal" font="default" size="100%">99</style></volume><pages><style face="normal" font="default" size="100%">919-935</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A dynamic-thermodynamic sea ice model is used to illustrate a sensitivity evaluation strategy in which a statistical model is fit to the output of the ice model. The statistical model response, evaluated in terms of certain metrics or integrated features of the ice model output, is a function of a selected set of d (= 13) prescribed parameters of the ice model and is therefore equivalent to a d-dimensional surface. The d parameters of the ice model are varied simultaneously in the sensitivity tests. The strongest sensitivities arise from the minimum lead fraction, the sensible heat exchange coefficient, and the atmospheric and oceanic drag coefficients. The statistical model shows that the interdependencies among these sensitivities are strong and physically plausible. A multidecadal simulation of Arctic sea ice is made using atmospheric forcing fields from 1960 to 1988 and parametric values from the approximate midpoints of the ranges sampled in the sensitivity tests. This simulation produces interannual variations consistent with submarine-derived data on ice thickness from 1976 and 1987 and with ice extent variations obtained from satellite passive microwave data. The ice model results indicate that (1) interannual variability is a major contributor to the differences of ice thickness and extent over timescales of a decade or less, and (2) the timescales of ice thickness anomalies are much longer than those of ice-covered areas. However, the simulated variations of ice coverage have less than 50% of their variance in common with observational data, and the temporal correlations between simulated and observed anomalies of ice coverage vary strongly with longitude.A dynamic-thermodynamic sea ice model is used to illustrate a sensitivity evaluation strategy in which a statistical model is fit to the output of the ice model. The statistical model response, evaluated in terms of certain metrics or integrated features of the ice model output, is a function of a selected set of d (= 13) prescribed parameters of the ice model and is therefore equivalent to a d-dimensional surface. The d parameters of the ice model are varied simultaneously in the sensitivity tests. The strongest sensitivities arise from the minimum lead fraction, the sensible heat exchange coefficient, and the atmospheric and oceanic drag coefficients. The statistical model shows that the interdependencies among these sensitivities are strong and physically plausible. A multidecadal simulation of Arctic sea ice is made using atmospheric forcing fields from 1960 to 1988 and parametric values from the approximate midpoints of the ranges sampled in the sensitivity tests. This simulation produces interannual variations consistent with submarine-derived data on ice thickness from 1976 and 1987 and with ice extent variations obtained from satellite passive microwave data. The ice model results indicate that (1) interannual variability is a major contributor to the differences of ice thickness and extent over timescales of a decade or less, and (2) the timescales of ice thickness anomalies are much longer than those of ice-covered areas. However, the simulated variations of ice coverage have less than 50% of their variance in common with observational data, and the temporal correlations between simulated and observed anomalies of ice coverage vary strongly with longitude.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">919</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Marston, J.</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiworker Household Travel Demand</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automobile ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">Demographics</style></keyword><keyword><style  face="normal" font="default" size="100%">Employed</style></keyword><keyword><style  face="normal" font="default" size="100%">Highway travel</style></keyword><keyword><style  face="normal" font="default" size="100%">Households</style></keyword><keyword><style  face="normal" font="default" size="100%">Income</style></keyword><keyword><style  face="normal" font="default" size="100%">New products</style></keyword><keyword><style  face="normal" font="default" size="100%">Population density</style></keyword><keyword><style  face="normal" font="default" size="100%">Travel behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Travel surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Trip generation</style></keyword><keyword><style  face="normal" font="default" size="100%">Urban areas</style></keyword><keyword><style  face="normal" font="default" size="100%">Vehicle miles of travel</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nhts.ornl.gov/1990/doc/demographic.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Federal Highway Administration</style></publisher><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">30 p</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The purpose of this study is to examine the travel behavior and related characteristics of multiworker households (MWHs) (defined as households with at least two workers) and how they contribute to the ever-increasing demand for transportation services. On average they have incomes which exceed the national household average and often have multiple automobiles and as households they generate a considerable number of trips. The virtual dearth of previous studies of MWHs makes an overview of their characteristics and their travel behavior necessary. This study reveals that the number of MWHs has continued to grow, as has their use of highways; they are found in disproportionate numbers in low density urban areas distant from public transportation. They also have new vehicles, and drive each vehicle more miles than other households. As households, MWHs travel more than do other households. However, an individual worker’s ability and desire to travel is constrained by time factors, among others, and transportation use by MWHs, when calculated on a per worker basis, is relatively low.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">RICHARD L. SMITH</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multivariate Threshold Methods</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1993</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Christopher, Ed J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non - response Bias and Trip Generation Models</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bias (Statistics)</style></keyword><keyword><style  face="normal" font="default" size="100%">Travel surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Trip generation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1993</style></year></dates><publisher><style face="normal" font="default" size="100%">Transportation Research Board</style></publisher><pages><style face="normal" font="default" size="100%">64-70</style></pages><isbn><style face="normal" font="default" size="100%">0309055598</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is serious concern over the fact that travel surveys often overrepresent smaller households with higher incomes and better education levels and, in general, that nonresponse is nonrandom. However, when the data are used to build linear models, such as trip generation models, and the model is correctly specified, estimates of parameters are unbiased regardless of the nature of the respondents, and the issues of how response rates and nonresponse bias are ameliorated. The more important task then is the complete specification of the model, without leaving out variables that have some effect on the variable to be predicted. The theoretical basis for this reasoning is given along with an example of how bias may be assessed in estimates of trip generation model parameters. Some of the methods used are quite standard, but the manner in which these and other more nonstandard methods have been systematically put together to assess bias in estimates shows that careful model building, not concern over bias in the data, becomes the key issue in developing trip generation and other models.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Elliott, M. R.</style></author><author><style face="normal" font="default" size="100%">Raghunathan, T. E.</style></author><author><style face="normal" font="default" size="100%">Schenker, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Estimates from Multiple Surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Wiley StatsRef: Statistics Reference Online</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dual frame</style></keyword><keyword><style  face="normal" font="default" size="100%">imputation</style></keyword><keyword><style  face="normal" font="default" size="100%">missing data</style></keyword><keyword><style  face="normal" font="default" size="100%">non-probability samples</style></keyword><keyword><style  face="normal" font="default" size="100%">small-area estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighting</style></keyword></keywords><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Elliott%2C%20Raghunathan%2C%20%26%20Schenker%20for%20Wiley%20StatsRef.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Combining estimates from multiple surveys can be very useful, especially when the question of interest cannot be&amp;nbsp;addressed well by a single, existing survey. In this paper, we provide a brief review of methodology for combining&amp;nbsp;estimates, with a focus on dual frame, weighting-based, joint-modeling, missing-data, and small-area methods.&amp;nbsp;Many such methods are useful in situations outside the realm of combining estimates from surveys, such as&amp;nbsp;combining information from surveys with administrative data and combining probability-sample data with&amp;nbsp;non-probability sample, or “big” data. We also provide examples of comparability issues that must be kept in mind&amp;nbsp;when information from different sources is being combined.&lt;/p&gt;
</style></abstract><custom1><style face="normal" font="default" size="100%">&lt;p&gt;https://www.niss.org/sites/default/files/Elliott%2C%20Raghunathan%2C%20%26%20Schenker%20for%20Wiley%20StatsRef.pdf&lt;/p&gt;
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