<?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%">Erciulescu A.L.</style></author><author><style face="normal" font="default" size="100%">Berg E.</style></author><author><style face="normal" font="default" size="100%">Cecere W.</style></author><author><style face="normal" font="default" size="100%">Ghosh M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bivariate Hierarchical Bayesian Model for Estimating Cropland Cash Rental Rates at the County Level.</style></title><secondary-title><style face="normal" font="default" size="100%">Survey Methodology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">In Press</style></year></dates></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%">Erciulescu A.L.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fuller W.A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bootstrap Confidence Intervals for Small Area Proportions</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%">2018</style></year></dates><issue><style face="normal" font="default" size="100%">DOI 10.1093/jssam/smy014</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%">Troia, G. A.</style></author><author><style face="normal" font="default" size="100%">Olinghouse, N. G.</style></author><author><style face="normal" font="default" size="100%">Wilson, J.</style></author><author><style face="normal" font="default" size="100%">Stewart, K. O.</style></author><author><style face="normal" font="default" size="100%">Mo, Y.</style></author><author><style face="normal" font="default" size="100%">Hawkins, L.</style></author><author><style face="normal" font="default" size="100%">Kopke, R.A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Common Core Writing Standards: A descriptive study of content and alignment with a sample of former state standards</style></title><secondary-title><style face="normal" font="default" size="100%">Reading Horizons</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cruze N.B.</style></author><author><style face="normal" font="default" size="100%">Erciulescu A.L.</style></author><author><style face="normal" font="default" size="100%">Nandram B.</style></author><author><style face="normal" font="default" size="100%">Barboza W.J.</style></author><author><style face="normal" font="default" size="100%">Young L.J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Developments in Model-Based Estimation of County-Level Agricultural Estimates</style></title><secondary-title><style face="normal" font="default" size="100%">ICES V Proceedings. Alexandria, VA: American Statistical Association</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ww2.amstat.org/meetings/ices/2016/proceedings/131_ices15Final00229.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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. A. Rudnick</style></author><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">E. Yan</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">S. E. Stein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular &amp; Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">1341-1351</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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%">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>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%">Abbatiello, S.</style></author><author><style face="normal" font="default" size="100%">Feng, X.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Mani, DR</style></author><author><style face="normal" font="default" size="100%">Schilling, B</style></author><author><style face="normal" font="default" size="100%">Maclean, B</style></author><author><style face="normal" font="default" size="100%">Zimmerman, LJ</style></author><author><style face="normal" font="default" size="100%">Cusack, MP</style></author><author><style face="normal" font="default" size="100%">Hall, SC</style></author><author><style face="normal" font="default" size="100%">Addona, T</style></author><author><style face="normal" font="default" size="100%">Allen, S</style></author><author><style face="normal" font="default" size="100%">Dodder, NG</style></author><author><style face="normal" font="default" size="100%">Ghosh, M</style></author><author><style face="normal" font="default" size="100%">Held, JM</style></author><author><style face="normal" font="default" size="100%">Hedrick, V</style></author><author><style face="normal" font="default" size="100%">Inerowicz, HD</style></author><author><style face="normal" font="default" size="100%">Jackson, A</style></author><author><style face="normal" font="default" size="100%">Keshishian, H</style></author><author><style face="normal" font="default" size="100%">Kim, JW</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Riley, CP</style></author><author><style face="normal" font="default" size="100%">Rudnick, P</style></author><author><style face="normal" font="default" size="100%">Sadowski, P</style></author><author><style face="normal" font="default" size="100%">Shaddox, K</style></author><author><style face="normal" font="default" size="100%">Smith, D</style></author><author><style face="normal" font="default" size="100%">Tomazela, D</style></author><author><style face="normal" font="default" size="100%">Wahlander, A</style></author><author><style face="normal" font="default" size="100%">Waldemarson, S</style></author><author><style face="normal" font="default" size="100%">Whitwell, CA</style></author><author><style face="normal" font="default" size="100%">You, J</style></author><author><style face="normal" font="default" size="100%">Zhang, S</style></author><author><style face="normal" font="default" size="100%">Kinsinger, CR</style></author><author><style face="normal" font="default" size="100%">Mesri, M</style></author><author><style face="normal" font="default" size="100%">Rodriguez, H</style></author><author><style face="normal" font="default" size="100%">Borchers, CH</style></author><author><style face="normal" font="default" size="100%">Buck, C</style></author><author><style face="normal" font="default" size="100%">Fisher, SJ</style></author><author><style face="normal" font="default" size="100%">Gibson, BW</style></author><author><style face="normal" font="default" size="100%">Liebler, D</style></author><author><style face="normal" font="default" size="100%">Maccoss, M</style></author><author><style face="normal" font="default" size="100%">Neubert, TA</style></author><author><style face="normal" font="default" size="100%">Paulovich, A</style></author><author><style face="normal" font="default" size="100%">Regnier, F</style></author><author><style face="normal" font="default" size="100%">Skates, SJ</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Wang, M</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design, Implementation and Multisite Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS)</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular and Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">2623-2639</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation &amp;lt;0.15, peak width coefficient of variation &amp;lt;0.15, standard deviation of RT &amp;lt;0.15 min (9 s), and the RT drift &amp;lt;0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Isukapati, Isaac Kumar</style></author><author><style face="normal" font="default" size="100%">List, George F.</style></author><author><style face="normal" font="default" size="100%">Williams, Billy M</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%">Synthesizing route travel time distributions from segment travel time distributions</style></title><secondary-title><style face="normal" font="default" size="100%">Trans. Res. Rec.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2013</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">71–81</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%">Hughes-Oliver JM</style></author><author><style face="normal" font="default" size="100%">Brooks A</style></author><author><style face="normal" font="default" size="100%">Welch W</style></author><author><style face="normal" font="default" size="100%">Khaldei MG</style></author><author><style face="normal" font="default" size="100%">Hawkins DM</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Patil K</style></author><author><style face="normal" font="default" size="100%">Howell GW</style></author><author><style face="normal" font="default" size="100%">Ng RT</style></author><author><style face="normal" font="default" size="100%">Chu MT</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ChemModLab: A web-based cheminromates modeling laboratory</style></title><secondary-title><style face="normal" font="default" size="100%">Cheminformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">61-81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;ChemModLab, written by the ECCR @ NCSU consortium under NIH support, is a toolbox for fitting and assessing quantitative structure-activity relationships (QSARs). Its elements are: a cheminformatic front end used to supply molecular descriptors for use in modeling; a set of methods for fitting models; and methods for validating the resulting model. Compounds may be input as structures from which standard descriptors will be calculated using the freely available cheminformatic front end PowerMV; PowerMV also supports compound visualization. In addition, the user can directly input their own choices of descriptors, so the capability for comparing descriptors is effectively unlimited. The statistical methodologies comprise a comprehensive collection of approaches whose validity and utility have been accepted by experts in the fields. As far as possible, these tools are implemented in open-source software linked into the flexible R platform, giving the user the capability of applying many different QSAR modeling methods in a seamless way. As promising new QSAR methodologies emerge from the statistical and data-mining communities, they will be incorporated in the laboratory. The web site also incorporates links to public-domain data sets that can be used as test cases for proposed new modeling methods. The capabilities of ChemModLab are illustrated using a variety of biological responses, with different modeling methodologies being applied to each. These show clear differences in quality of the fitted QSAR model, and in computational requirements. The laboratory is web-based, and use is free. Researchers with new assay data, a new descriptor set, or a new modeling method may readily build QSAR models and benchmark their results against other findings. Users may also examine the diversity of the molecules identified by a QSAR model. Moreover, users have the choice of placing their data sets in a public area to facilitate communication with other researchers; or can keep them hidden to preserve confidentiality.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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%">G. F. List</style></author><author><style face="normal" font="default" size="100%">B. M. Williams</style></author><author><style face="normal" font="default" size="100%">N. M. Rouphail</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Forging an understanding of travel time reliability for freeway and arterial networks</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2012 International Symposium on Transportation Network Reliability (INSTR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</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%">Jianqiang C. Wang</style></author><author><style face="normal" font="default" size="100%">S. H. Holan</style></author><author><style face="normal" font="default" size="100%">Balgobin Nandram</style></author><author><style face="normal" font="default" size="100%">Wendy Barboza</style></author><author><style face="normal" font="default" size="100%">Criselda Toto</style></author><author><style face="normal" font="default" size="100%">Edwin Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys</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%">Bayesian hierarchical model</style></keyword><keyword><style  face="normal" font="default" size="100%">Composite estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic model</style></keyword><keyword><style  face="normal" font="default" size="100%">Forecasting Model comparison</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</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%">October 29, 2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">84-106</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</style></author><author><style face="normal" font="default" size="100%">Engel, R.</style></author><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">Gunning, P.</style></author><author><style face="normal" font="default" size="100%">Fleming, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Psychometric and Statistical Modeling for the Study of Retention and Graduation in Undergraduate Engineering</style></title><secondary-title><style face="normal" font="default" size="100%">Social Statistics and Higher Education Conference Volume</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. Oganyan</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">M.-J. Woo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global measures of data utility for microdata masked for disclosure limitation</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Privacy and Confidentiality</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">111-124</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%">Mi-ja Woo</style></author><author><style face="normal" font="default" size="100%">Jerome Reiter</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%">Estimation of propensity scores using generalized additive models</style></title><secondary-title><style face="normal" font="default" size="100%">Statisics in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">3806-3816</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M.J. Bayarri</style></author><author><style face="normal" font="default" size="100%">J. Berger</style></author><author><style face="normal" font="default" size="100%">Garcia-Donato, G.</style></author><author><style face="normal" font="default" size="100%">Liu, F.</style></author><author><style face="normal" font="default" size="100%">R. Paulo</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Palomo, J.</style></author><author><style face="normal" font="default" size="100%">Walsh, D.</style></author><author><style face="normal" font="default" size="100%">J. Cafeo</style></author><author><style face="normal" font="default" size="100%">Parthasarathy, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer Model Validation with Functional Output</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of  Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">1874-190</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, X. S.</style></author><author><style face="normal" font="default" size="100%">Salloum, G.A.</style></author><author><style face="normal" font="default" size="100%">Chipman, H.A.</style></author><author><style face="normal" font="default" size="100%">Welch, W.J.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploration of cluster structure-activity relationship analysis in efficient high-throughput screening</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1206-1214</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Sequential screening has become increasingly popular in drug discovery. It iteratively builds quantitative structure-activity relationship (QSAR) models from successive high-throughput screens, making screening more effective and efficient. We compare cluster structure-activity relationship analysis (CSARA) as a QSAR method with recursive partitioning (RP), by designing three strategies for sequential collection and analysis of screening data. Various descriptor sets are used in the QSAR models to characterize chemical structure, including high-dimensional sets and some that by design have many variables not related to activity. The results show that CSARA outperforms RP. We also extend the CSARA method to deal with a continuous assay measurement.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Xiaodong Lin</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author><author><style face="normal" font="default" size="100%">Jerome P. Reiter</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">D. Olwell</style></author><author><style face="normal" font="default" size="100%">A. G.Wilson</style></author><author><style face="normal" font="default" size="100%">G. Wilson</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure statistical analysis of distributed databases using partially trusted third parties. Manuscript in preparation</style></title><secondary-title><style face="normal" font="default" size="100%">In Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer–Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">New York</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Wan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traffic Simulation Failure Detection and Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">North Carolina State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Raleigh</style></pub-location><volume><style face="normal" font="default" size="100%">Ph.D.</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">masters</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hawkins, D.M.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, R.D.</style></author><author><style face="normal" font="default" size="100%">L. Liu</style></author><author><style face="normal" font="default" size="100%">Young. S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring blood spectra for signs of ovarian cancer</style></title><secondary-title><style face="normal" font="default" size="100%">Chance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">19-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Claudia Tebaldi</style></author><author><style face="normal" font="default" size="100%">Mike West</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%">Statistical Analyses of Freeway Traffic Flows</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Forecasting</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%">21</style></volume><pages><style face="normal" font="default" size="100%">39–68</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%">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%">Woodard, R.</style></author><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author><author><style face="normal" font="default" size="100%">Sheriff, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A bivariate Bayes method for improving the estimates of mortality rates with a twofold conditional autoregressive model</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural Biological and Environmental Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Missouri Turkey Hunting Survey (MTHS) is a post-season mail survey conducted by the Missouri Department of Conservation to monitor and aid in the regulation of the turkey hunting season. Questionnaires are distributed after the hunting season to a simple random sample of persons who purchased permits to hunt wild turkey during the spring season. For the 1996 turkey hunting season 95,801 persons purchased hunting permits. From these individuals a simple random sample of 6,999 hunters were selected for the survey and 5,005 of these responded. The MTHS 1 Roger Woodard (E-mail: woodard@stat.missouri.edu) is a Ph.D student and Dongchu Sun (E-mail: dsun@stat.missouri.edu) is Associate Professor of Statistics, Department of Statistics, University of Missouri, Columbia, MO 65211. Zhuoqiong He (E-mail: HEZ@mail.conservation.state.mo.us) is a biometrician and Steven L. Sheri (E-mail: SHERIS@mail.conservation.state.mo.us) is a wildlife biometrics superv&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Valerie S. L. Williams</style></author><author><style face="normal" font="default" size="100%">Lyle V. Jones</style></author><author><style face="normal" font="default" size="100%">John W. Tukey</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Controlling error in multiple comparisons, with special attention to the national assessment of educational progress</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Educational and Behavioral Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">42–69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Three alternative procedures to adjust significance levels for multiplicity are the traditional Bonferroni technique, a sequential Bonferroni technique devel-oped by Hochberg (1988), and a sequential approach for controlling the false discovery rate proposed by Benjamini and Hochberg (1995). These procedures are illustrated and compared using examples from the National Assessment of Educational Progress (NAEP). A prominent advantage of the Benjamini and Hochberg (B-H) procedure, as demonstrated in these examples, is the greater invariance of statistical significance for given comparisons over alternative family sizes. Simulation studies show that all three procedures maintain a false discovery rate bounded above, often grossly, by ct (or c&amp;nbsp;/2). For both uncorre-lated and pairwise families of comparisons, the B-H technique is shown to have greater power than the Hochberg or Bonferroni procedures, and its power remains relatively stable as the number of comparisons becomes large, giving it an increasing advantage when many comparisons are involved. We recommend that results from NAEP State Assessments be reported using the B-H technique rather than the Bonferroni procedure. Two questions often asked about each of a set of observed comparisons are: (a) should we be confident about the direction or the sign of the corresponding underlying population comparison, and (b) for what interval of values should we be confident that it contains the value for the population comparison?&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abt, Markus</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design and Analysis for Modeling and Predicting Spatial Contamination</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical Geology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">best linear unbiased prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">dioxin contamination</style></keyword><keyword><style  face="normal" font="default" size="100%">Gaussian stochastic process</style></keyword><keyword><style  face="normal" font="default" size="100%">lognormal kriging</style></keyword><keyword><style  face="normal" font="default" size="100%">ordinary kriging</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial statistics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1023/A%3A1007504329298</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers-Plenum Publishers</style></publisher><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">1-22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Sampling and prediction strategies relevant at the planning stage of the cleanup of environmental hazards are discussed. Sampling designs and models are compared using an extensive set of data on dioxin contamination at Piazza Road, Missouri. To meet the assumptions of the statistical model, such data are often transformed by taking logarithms. Predicted values may be required on the untransformed scale, however, and several predictors are also compared. Fairly small designs turn out to be sufficient for model fitting and for predicting. For fitting, taking replicates ensures a positive measurement error variance and smooths the predictor. This is strongly advised for standard predictors. Alternatively, we propose a predictor linear in the untransformed data, with coefficients derived from a model fitted to the logarithms of the data. It performs well on the Piazza Road data, even with no replication.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Claudia Tebaldi</style></author><author><style face="normal" font="default" size="100%">Michael West</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Inference on Network Traffic Using Link Count Data</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%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/1998</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.jstor.org/stable/2670105http://www.jstor.org/stable/2670105</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">93</style></volume><pages><style face="normal" font="default" size="100%">557-573</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We study Bayesian models and methods for analysing network traffic counts in problems of inference about the traffic intensity between directed pairs of origins and destinations in networks. This is a class of problems very recently discussed by Vardi in a 1996 JASA article and is of interest in both communication and transportation network studies. The current article develops the theoretical framework of variants of the origin-destination flow problem and introduces Bayesian approaches to analysis and inference. In the first, the so-called fixed routing problem, traffic or messages pass between nodes in a network, with each message originating at a specific source node, and ultimately moving through the network to a predetermined destination node. All nodes are candidate origin and destination points. The framework assumes no travel time complications, considering only the number of messages passing between pairs of nodes in a specified time interval. The route count, or route flow, problem is to infer the set of actual number of messages passed between each directed origin-destination pair in the time interval, based on the observed counts flowing between all directed pairs of adjacent nodes. Based on some development of the theoretical structure of the problem and assumptions about prior distributional forms, we develop posterior distributions for inference on actual origin-destination counts and associated flow rates. This involves iterative simulation methods, or Markov chain Monte Carlo (MCMC), that combine Metropolis-Hastings steps within an overall Gibbs sampling framework. We discuss issues of convergence and related practical matters, and illustrate the approach in a network previously studied in Vardi’s article. We explore both methodological and applied aspects much further in a concrete problem of a road network in North Carolina, studied in transportation flow assessment contexts by civil engineers. This investigation generates critical insight into limitations of statistical analysis, and particularly of non-Bayesian approaches, due to inherent structural features of the problem. A truly Bayesian approach, imposing partial stochastic constraints through informed prior distributions, offers a way of resolving these problems and is consistent with prevailing trends in updating traffic flow intensities in this field. Following this, we explore a second version of the problem that introduces elements of uncertainty about routes taken by individual messages in terms of Markov selection of outgoing links for messages at any given node. For specified route choice probabilities, we introduce the concept of a super-network-namely, a fixed routing problem in which the stochastic problem may be embedded. This leads to solution of the stochastic version of the problem using the methods developed for the original formulation of the fixed routing problem. This is also illustrated. Finally, we discuss various related issues and model extensions, including inference on stochastic route choice selection probabilities, questions of missing data and partially observed link counts, and relationships with current research on road traffic network problems in which travel times within links are nonnegligible and may be estimated from additional data.&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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aslett, Robert</style></author><author><style face="normal" font="default" size="100%">Buck, Robert J.</style></author><author><style face="normal" font="default" size="100%">Duvall, Steven G.</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Circuit optimization via sequential computer experiments: design of an output buffer</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Royal Statistical Society: Series C</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Circuit simulator</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer code</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer model</style></keyword><keyword><style  face="normal" font="default" size="100%">Engineering design</style></keyword><keyword><style  face="normal" font="default" size="100%">Parameter design</style></keyword><keyword><style  face="normal" font="default" size="100%">Stochastic process</style></keyword><keyword><style  face="normal" font="default" size="100%">Visualization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">31-48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In electrical engineering, circuit designs are now often optimized via circuit simulation computer models. Typically, many response variables characterize the circuit’s performance. Each response is a function of many input variables, including factors that can be set in the engineering design and noise factors representing manufacturing conditions. We describe a modelling approach which is appropriate for the simulator’s deterministic input–output relationships. Non-linearities and interactions are identified without explicit assumptions about the functional form. These models lead to predictors to guide the reduction of the ranges of the designable factors in a sequence of experiments. Ultimately, the predictors are used to optimize the engineering design. We also show how a visualization of the fitted relationships facilitates an understanding of the engineering trade-offs between responses. The example used to demonstrate these methods, the design of a buffer circuit, has multiple targets for the responses, representing different trade-offs between the key performance measures.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Schonlau</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author><author><style face="normal" font="default" size="100%">Jones, Donald R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global versus Local Search in Constrained Optimization of Computer Models</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes-Monograph Series</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian global optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer code</style></keyword><keyword><style  face="normal" font="default" size="100%">sequential design</style></keyword><keyword><style  face="normal" font="default" size="100%">Stochastic process</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">11-25</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Engineering systems are now frequently optimized via computer models. The input-output relationships in these models are often highly nonlinear deterministic functions that are expensive to compute. Thus, when searching for the global optimum, it is desirable to minimize the number of function evaluations. Bayesian global optimization methods are well-suited to this task because they make use of all previous evaluations in selecting the next search point. A statistical model is fit to the sampled points which allows predictions to be made elsewhere, along with a measure of possible prediction error (uncertainty). The next point is chosen to maximize a criterion that balances searching where the predicted value of the function is good (local search) with searching where the uncertainty of prediction is large (global search). We extend this methodology in several ways. First, we introduce a parameter that controls the local-global balance. Secondly, we propose a method for dealing with nonlinear inequality constraints from additional response variables. Lastly, we adapt the sequential algorithm to proceed in stages rather than one point at a time. The extensions are illustrated using a shape optimization problem from the automotive industry.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Williams, Valerie</style></author><author><style face="normal" font="default" size="100%">Billeaud, Kathleen</style></author><author><style face="normal" font="default" size="100%">Davis, Lori A.</style></author><author><style face="normal" font="default" size="100%">Thissen, David</style></author><author><style face="normal" font="default" size="100%">Sanford, Eleanor E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Projecting to the NAEP Scale: Results from the North Carolina End-of-Grade Testing Program</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Educational Measurement</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">277-296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Data from the North Carolina End-of-Grade test of eighth-grade mathematics are used to estimate the achievement results on the scale of the National Assessment of Educational Progress (NAEP) Trial State Assessment. Linear regression models are used to develop projection equations to predict state NAEP results in the future, and the results of such predictions are compared with those obtained in the 1996 administration of NAEP. Standard errors of the parameter estimates are obtained using a bootstrap resampling technique.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of Parameters  in Mechanistic Models:  A Case Study of PCB Fate and Transport in Surface Waters</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">97</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">K. Wang</style></author><author><style face="normal" font="default" size="100%">D.C. Jansen</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Permeability study of cracked concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Cement Concrete Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">381-393</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cracks in concrete generally interconnect flow paths and increase concrete permeability. The increase in concrete permeability due to the progression of cracks allows more water or aggressive chemical ions to penetrate into the concrete, facilitating deterioration. The present work studies the relationship between crack characteristics and concrete permeability. In this study, feedback controlled splitting tests are introduced to generate crack width-controlled concrete specimens. Sequential crack patterns with different crack widths are viewed under a microscope. The permeability of cracked concrete is evaluated by water permeability tests. The preliminary results indicate that crack openings generally accelerate water flow rate in concrete. When a specimen is loaded to have a crack opening displacement smaller than 50 microns prior to unloading, the crack opening has little effect on concrete permeability. When the crack opening displacement increases from 50 microns to about 200 microns, concrete permeability increases rapidly. After the crack opening displacement reaches 200 microns, the rate of water permeability increases steadily. The present research may provide insight into developing design criteria for a durable concrete and in predicting service life of a concrete structure.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Model for Fate and  Transport of Polychlorinated Biphenyl in Upper Hudson River</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Environmental Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">122</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Model for Fate and Transport of Polychlorinated Biphenyl in Upper Hudson River</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Environmental Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Hudson River</style></keyword><keyword><style  face="normal" font="default" size="100%">PCB</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation models</style></keyword><keyword><style  face="normal" font="default" size="100%">transport phenomena</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May 1996</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">341-349</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modelers of contaminant fate and transport in surface waters typically rely on literature values when selecting parameter values for mechanistic models. While the expert judgment with which these selections are made is valuable, the information contained in contaminant concentration measurements should not be ignored. In this full-scale Bayesian analysis of polychlorinated biphenyl (PCB) contamination in the upper Hudson River, these two sources of information are combined using Bayes’ theorem. A simulation model for the fate and transport of the PCBs in the upper Hudson River forms the basis of the likelihood function while the prior density is developed from literature values. The method provides estimates for the anaerobic biodegradation half-life, aerobic biodegradation plus volatilization half-life, contaminated sediment depth, and resuspension velocity of 4,400 d, 3.2 d, 0.32 m, and 0.02 m/yr, respectively. These are significantly different than values obtained with more traditional methods, and are shown to produce better predictions than those methods when used in a cross-validation study.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gao, Feng</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Welch, William</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting ozone levels and trends with semiparametric modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural, Biological, and Environmental Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">404-425</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">404</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">Gough, William A.</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parameter space exploration of an ocean general circulation model using an isopycnal mixing parameterization</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Marine Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><volume><style face="normal" font="default" size="100%">52</style></volume><pages><style face="normal" font="default" size="100%">773-796</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this study we have employed statistical methods to efficiently design experiments and analyze output of an ocean general circulation model that uses an isopycnal mixing parameterization. Full ranges of seven inputs are explored using 51 numerical experiments. Fifteen of the cases fail to reach satisfactory equilibria. These are attributable to numerical limitations specific to the isopycnal model. Statistical approximating functions are evaluated using the remaining cases to determine the dependency of each of the six scalar outputs on the inputs. With the exception of one output, the approximating functions perform well. Known sensitivities, particularly the importance of diapycnal (vertical) eddy diffusivity and wind stress, are reproduced. The sensitivities of the model to two numerical constraints specific to the isopycnal parameterization, maximum allowable isopycnal slope and horizontal background eddy diffusivity, are explored. Isopycnal modelling issues, convection reduction and the Veronis effect, are examined and found to depend crucially on the isopycnal modelling constraints.</style></abstract></record></records></xml>