<?xml version="1.0" encoding="UTF-8"?><xml><records><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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leu,D.</style></author><author><style face="normal" font="default" size="100%">Coiro, J.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</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%">Using the Psychometric Characteristics of Multiple-Choice, Open Internet, and Closed (Simulated) Internet Formats to Refine the Development of Online Research and Comprehension Assessments in Science: Year Three of the ORCA Project</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%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">L. H. Cox</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The World’s Simplest Survey Microsimulator (WSSM)</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%">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%">Bellow, Michael E.</style></author><author><style face="normal" font="default" size="100%">Cruze, Nathan</style></author><author><style face="normal" font="default" size="100%">Erciulescu, Andreea L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Developments in Model-Based County Level Estimation of Agricultural Cash Rental Rates</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings. Survey Research Methods Section. Alexandria, VA: American Statistical Association.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/2017%20-%20Developments%20in%20Model-Based%20County-Level%20Estimation%20of%20Ag%20Cash%20Rental%20Rates.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">2773 - 2790</style></section></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%">Erciulescu, Andreea L.</style></author><author><style face="normal" font="default" size="100%">Cruze, Nathan B.</style></author><author><style face="normal" font="default" size="100%">Nandram, Balgobin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Small Area Estimates for End-of-Season Agricultural Quantities</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings. Survey Research Methods Section. Alexandria, VA: American Statistical Association.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/2017%20-%20Small%20Area%20Estimates%20for%20End-Of-Season%20Agricultural%20Quantities.pdf</style></url></web-urls></urls><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%">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%">H. J. Kim</style></author><author><style face="normal" font="default" size="100%">L. H. Cox</style></author><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><author><style face="normal" font="default" size="100%">Q. Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous Edit-Imputation for Continuous Microdata</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the American Statistical Association</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">110</style></volume><pages><style face="normal" font="default" size="100%">987-999</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%">Lawrence H. 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F. Karr</style></author><author><style face="normal" font="default" size="100%">H. J. Kim</style></author><author><style face="normal" font="default" size="100%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">Q. Wang</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple imputation of missing or faulty values under linear constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Business Economic Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">American Statistical Association</style></publisher><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">375-386</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>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%">H. J. Kim</style></author><author><style face="normal" font="default" size="100%">Karr Alan F</style></author><author><style face="normal" font="default" size="100%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">Q. Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous Edit-Imputation for Continuous Microdata</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">189</style></number><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%">I. A. Carrillo</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%">Combining cohorts in longitudinal surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Survey Methodology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Joint-randomization inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-cohort longitudinal surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Replication variance estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Rotating panel surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Superpopulation parameters</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighted Generalized Estimating Equations</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">149-182</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 question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization frame work for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation&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>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%">Z. He</style></author><author><style face="normal" font="default" size="100%">M. P. Cohen</style></author><author><style face="normal" font="default" size="100%">D. Battle</style></author><author><style face="normal" font="default" size="100%">D. L. Achorn</style></author><author><style face="normal" font="default" size="100%">A. D. McKay</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Construction of replicate weights for Project TALENT</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings, Section on Survey Research Methods 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</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%">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>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%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">S. K. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The World’s Simplest Survey Microsimulator (WSSM)</style></title><secondary-title><style face="normal" font="default" size="100%">The World’s Simplest Survey Microsimulator (WSSM)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.fcsm.gov/12papers/Karr_2012FCSM_II-A.pdf</style></url></web-urls></urls><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%">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>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%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">S. K. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Risk-utility paradigms for statistical disclosure limitation: How to think, but not how to act (with discussion)</style></title><secondary-title><style face="normal" font="default" size="100%">International Statistical Review</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%">2</style></number><volume><style face="normal" font="default" size="100%">79</style></volume><pages><style face="normal" font="default" size="100%">160-199</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Risk-utility formulations for problems of statistical disclosure limitation are now common. We argue that these approaches are powerful guides to official statistics agencies in regard to how to think about disclosure limitation problems, but that they fall short in essential ways from providing a sound basis for acting upon the problems. We illustrate this position in three specific contexts—transparency, tabular data and survey weights, with shorter consideration of two key emerging issues—longitudinal data and the use of administrative data to augment surveys.&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%">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%">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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. Chen</style></author><author><style face="normal" font="default" size="100%">L. Brandt</style></author><author><style face="normal" font="default" size="100%">V. Gregg</style></author><author><style face="normal" font="default" size="100%">R. Traunmüller</style></author><author><style face="normal" font="default" size="100%">S. Dawes</style></author><author><style face="normal" font="default" size="100%">E. Hovy</style></author><author><style face="normal" font="default" size="100%">A. Macintosh</style></author><author><style face="normal" font="default" size="100%">C. A. Larson</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Citizen access to government statistical information</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">503-529</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern electronic technologies have dramatically increased the volume of information collected and assembled by government agencies at all levels. This chapter describes digital government research aimed at keeping government data warehouses from turning into data cemeteries. The products of the research exploit modern electronic technologies in order to allow “ordinary citizens” and researchers access to government-assembled information. The goal is to help ensure that more data also means better and more useful data. Underlying the chapter are three tensions. The first is between comprehensiveness and understandability of information available to non-technically oriented “private citizens.” The second is between ensuring usefulness of detailed statistical information and protecting confidentiality of data subjects. The third tension is between the need to analyze “global” data sets and the reality that government data are distributed among both levels of government and agencies (typically, by the “domain” of data, such as education, health, or transportation).&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">25</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%">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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jeffrey D. Picka</style></author><author><style face="normal" font="default" size="100%">Chermakani, Karthik</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Random-walk-based estimates of transport properties in small specimens of composite materials</style></title><secondary-title><style face="normal" font="default" size="100%">Phys Rev E Stat Nonlin Soft Matter Phys</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%">random walks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</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;A method based on random walks is developed for estimating the dc conductance and similar transport properties in small specimens of composite materials. The method is valid over a much wider range of material structures than are asymptotic methods, and requires only that the internal structure of the material be known. The error in its estimates is limited primarily by CPU speed. It is found to work best for composites consisting of a bulk conducting phase and inclusions of lower conductivity.&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>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%">Graham, Jinko</style></author><author><style face="normal" font="default" size="100%">Curran, James</style></author><author><style face="normal" font="default" size="100%">Weir, Bruce</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conditional Genotypic Probabilities for Microsatellite Loci</style></title><secondary-title><style face="normal" font="default" size="100%">Genetics</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%">155</style></volume><pages><style face="normal" font="default" size="100%">1973-1980</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern forensic DNA profiles are constructed using microsatellites, short tandem repeats of 2-5 bases. In the absence of genetic data on a crime-specific subpopulation, one tool for evaluating profile evidence is the match probability. The match probability is the conditional probability that a random person would have the profile of interest given that the suspect has it and that these people are different members of the same subpopulation. One issue in evaluating the match probability is population differentiation, which can induce coancestry among subpopulation members. Forensic assessments that ignore coancestry typically overstate the strength of evidence against the suspect. Theory has been developed to account for coancestry; assumptions include a steady-state population and a mutation model in which the allelic state after a mutation event is independent of the prior state. Under these assumptions, the joint allelic probabilities within a subpopulation may be approximated by the moments of a Dirichlet distribution. We investigate the adequacy of this approximation for profiled loci that mutate according to a generalized stepwise model. Simulations suggest that the Dirichlet theory can still overstate the evidence against a suspect with a common microsatellite genotype. However, Dirichlet-based estimators were less biased than the product-rule estimator, which ignores coancestry.&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%">Kitamura, Ryuichi</style></author><author><style face="normal" font="default" size="100%">Chen, Cynthia</style></author><author><style face="normal" font="default" size="100%">Pendyala, Ram M.</style></author><author><style face="normal" font="default" size="100%">Narayanan, Ravi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Micro-simulation of daily activity-travel patterns for travel demand forecasting</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">daily activity-travel patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">forecasting</style></keyword><keyword><style  face="normal" font="default" size="100%">micro-simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic travel patterns</style></keyword></keywords><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/10.1023/A%3A1005259324588</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</style></publisher><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">25-51</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 development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals’ daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns.&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%">Susan Paddock</style></author><author><style face="normal" font="default" size="100%">Michael West</style></author><author><style face="normal" font="default" size="100%">S. Stanley Young</style></author><author><style face="normal" font="default" size="100%">M. Clyde</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Mixture Models in Exploration of Structure-Activity Relationships in Drug Design</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Science and Technology: Case Studies 4</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</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%">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%">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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author><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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Introduction: Problems in Environmental Monitoring and Assessment</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_1</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%">1-4</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;The need for innovative statistical methods for modern environmental assessment is undisputed. The case studies in this book are a sampling of the broad sweep of statistical applications available in the environmental sciences, targeted to environmental monitoring and assessment. A unique feature of the applications presented here is that they are not isolated projects but were, instead, fostered under a long-term collaborative association between the U.S. Environmental Protection Agency (EPA) and the National Institute of Statistical Sciences (NISS). This institutional support resulted in a strong interdisciplinary component to the research, and common threads of statistical methodology and data analysis principles are seen across all of the projects. The case studies necessarily are detailed and technical and so this introductory chapter will give an overview of what follows and emphasize common themes that tie the projects together. Research, by its very nature, does not follow a direct path and depends on past results for the next step. This process is enriched through the collaboration of statisticians with other scientists.&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%">Kitamura, Ryuichi</style></author><author><style face="normal" font="default" size="100%">Chen, Cynthia</style></author><author><style face="normal" font="default" size="100%">Narayanan, Ravi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traveler Destination Choice Behavior: Effects of Time of Day, Activity Duration and Home Location</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Choice models</style></keyword><keyword><style  face="normal" font="default" size="100%">Hypothesis testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Logits</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial logits</style></keyword><keyword><style  face="normal" font="default" size="100%">Origin and destination</style></keyword><keyword><style  face="normal" font="default" size="100%">Periods of the day</style></keyword><keyword><style  face="normal" font="default" size="100%">Residential location</style></keyword><keyword><style  face="normal" font="default" size="100%">Time duration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><pages><style face="normal" font="default" size="100%">76-81</style></pages><isbn><style face="normal" font="default" size="100%">0309065178</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multinomial logit destination choice models are developed and the following hypotheses are examined: (a) time of day affects destination choice behavior, (b) the duration of stay at the destination affects destination choice, and (c) home location affects non-home-based destination choice. The statistical results offer strong evidence in support of the hypotheses.&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%">Lawrence H. Cox</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%">Workshop: Statistical Methods for Combining Environmental Information</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_8</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%">143-158</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;Primary objectives of the NISS-USEPA cooperative research agreement were to identify important environmental problems to which statistical science could contribute, to perform interdisciplinary research on these problems and stimulate related research and problem identification within the broader statistical community, to assess important examples and areas of environmetric research, and to identify new research problems and directions. To provide a forum for identifying and examining new research and problem areas, a NISS-USEPA workshop series was established within the cooperative research program.&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%">Waller, Lance A.</style></author><author><style face="normal" font="default" size="100%">Louis, Thomas A.</style></author><author><style face="normal" font="default" size="100%">Carlin, Bradley P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayes methods for combining disease and exposure data in assessing environmental justice</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental and Ecological Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">environmental equity</style></keyword><keyword><style  face="normal" font="default" size="100%">hierarchical model</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov chain Monte Carlo</style></keyword><keyword><style  face="normal" font="default" size="100%">regulation</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/10.1023/A%3A1018586715034</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">267-281</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Environmental justice reflects the equitable distribution of the burden of environmental hazards across various sociodemographic groups. The issue is important in environmental regulation, siting of hazardous waste repositories and prioritizing remediation of existing sources of exposure. We propose a statistical framework for assessing environmental justice. The framework includes a quantitative assessment of environmental equity based on the cumulative distribution of exposure within population subgroups linked to disease incidence through a dose-response function. This approach avoids arbitrary binary classifications of individuals solely as ’exposed’ or ’unexposed’. We present a Bayesian inferential approach, implemented using Markov chain Monte Carlo methods, that accounts for uncertainty in both exposure and response. We illustrate our method using data on leukemia deaths and exposure to toxic chemical releases in Allegheny County, Pennsylvania.&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%">Dennis D. Cox</style></author><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author><author><style face="normal" font="default" size="100%">ENSOR, KATHERINE B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial sampling and the environment: some issues and directions</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental and Ecological Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">environmental monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">experimental design</style></keyword><keyword><style  face="normal" font="default" size="100%">kriging</style></keyword><keyword><style  face="normal" font="default" size="100%">multiphase sampling</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial statistics</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/10.1023/A%3A1018578513217</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">219-233</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%">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%">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%">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;
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