<?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%">Nandram B.</style></author><author><style face="normal" font="default" size="100%">Erciulescu A.L.</style></author><author><style face="normal" font="default" size="100%">Cruze N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Benchmarking of the Fay-Herriot Model Using Random Deletion.</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><author><style face="normal" font="default" size="100%">Cruze N.</style></author><author><style face="normal" font="default" size="100%">Nandram B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmarking a Triplet of Official Estimates.</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental and Ecological Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><number><style face="normal" font="default" size="100%">DOI 10.1007/s10651-018-0416-4</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">523-547</style></pages><issue><style face="normal" font="default" size="100%">4</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%">Erciulescu A.L.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Cruze N.</style></author><author><style face="normal" font="default" size="100%">Nandram B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based County-Level Crop Estimates Incorporating Auxiliary Sources of Information</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Royal Statistical Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Journal of the Royal Statistical Society</style></publisher><volume><style face="normal" font="default" size="100%">Series A</style></volume><issue><style face="normal" font="default" size="100%">DOI 10.1111/rssa.12390</style></issue></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%">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%">Erciulescu A.L.</style></author><author><style face="normal" font="default" size="100%">Cruze N.B.</style></author><author><style face="normal" font="default" size="100%">Nandram B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-based County-Level Crop Estimates Incorporating Auxiliary Sources of Information</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%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ww2.amstat.org/MembersOnly/proceedings/2016/data/assets/pdf/389784.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%">3591-3605 </style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J. Lane</style></author><author><style face="normal" font="default" size="100%">V. Stodden</style></author><author><style face="normal" font="default" size="100%">H. Nissenbaum</style></author><author><style face="normal" font="default" size="100%">S. Bender</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Confidentiality and Data Access in the Use of Big Data: Theory and Practical Approaches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Cambridge University Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">Analytical frameworks for data release: A statistical view</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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%">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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Neuman, S.</style></author><author><style face="normal" font="default" size="100%">Gambrell, L.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The New Literacies of Online Research and Comprehension: Assessing and Preparing Students for the 21st Century with Common Core State Standards</style></title><secondary-title><style face="normal" font="default" size="100%">Quality Reading Instruction in the Age of Common Core Standards</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">International Reading Association</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">16</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">D. L. Banks</style></author><author><style face="normal" font="default" size="100%">G. Datta</style></author><author><style face="normal" font="default" size="100%">J. Lynch</style></author><author><style face="normal" font="default" size="100%">J. Niemi</style></author><author><style face="normal" font="default" size="100%">F. Vera</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian CAR models for syndromic surveillance on multiple data streams: Theory and practice</style></title><secondary-title><style face="normal" font="default" size="100%">Information Fusion</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayes</style></keyword><keyword><style  face="normal" font="default" size="100%">CAR models</style></keyword><keyword><style  face="normal" font="default" size="100%">Gibbs distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random field</style></keyword><keyword><style  face="normal" font="default" size="100%">Syndromic surveillance</style></keyword></keywords><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://dx.doi.org/10.1016/j.inffus.2009.10.005</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">105–116</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Syndromic surveillance has, so far, considered only simple models for Bayesian inference. This paper details the methodology for a serious, scalable solution to the problem of combining symptom data from a network of US hospitals for early detection of disease outbreaks. The approach requires high-end Bayesian modeling and significant computation, but the strategy described in this paper appears to be feasible and offers attractive advantages over the methods that are currently used in this area. The method is illustrated by application to ten quarters worth of data on opioid drug abuse surveillance from 636 reporting centers, and then compared to two other syndromic surveillance methods using simulation to create known signal in the drug abuse database.&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%">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>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%">H. T. Banks</style></author><author><style face="normal" font="default" size="100%">H. K. Nguyen</style></author><author><style face="normal" font="default" size="100%">J. R. Samuels, Jr.</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sensitivity to noise variance in a social network dynamics model</style></title><secondary-title><style face="normal" font="default" size="100%">Q. Applied Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">66</style></volume><pages><style face="normal" font="default" size="100%">233-247</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. E. Fienberg</style></author><author><style face="normal" font="default" size="100%">Y. Nardi</style></author><author><style face="normal" font="default" size="100%">A. Slavkovic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure logistic regression with distributed databases</style></title><secondary-title><style face="normal" font="default" size="100%">Bulletin of International Statistics Institute</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Ge N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of diversity and focused combinatorial libraries in drug discovery</style></title><secondary-title><style face="normal" font="default" size="100%">Current Opinion in Drug Discovery &amp; Development</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">318-324</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%">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%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">P. Thakuriah</style></author><author><style face="normal" font="default" size="100%">A. Sen</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">R. Emmerink</style></author><author><style face="normal" font="default" size="100%">P. Nijkamp</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Probe-based surveillance for travel time information in ITS</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Ashgate Publishing Ltd</style></publisher><pages><style face="normal" font="default" size="100%">393-425</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">17</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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%">Nobile, Agostino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A hybrid Markov chain for the Bayesian analysis of the multinomial probit model</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics and Computing</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%">Gibbs sampling</style></keyword><keyword><style  face="normal" font="default" size="100%">Metropolis algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1023/A%3A1008905311214</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%">8</style></volume><pages><style face="normal" font="default" size="100%">229-242</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Bayesian inference for the multinomial probit model, using the Gibbs sampler with data augmentation, has been recently considered by some authors. The present paper introduces a modification of the sampling technique, by defining a hybrid Markov chain in which, after each Gibbs sampling cycle, a Metropolis step is carried out along a direction of constant likelihood. Examples with simulated data sets motivate and illustrate the new technique. A proof of the ergodicity of the hybrid Markov chain is also given.&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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nobile, Agostino</style></author><author><style face="normal" font="default" size="100%">Bhat, Chandra R.</style></author><author><style face="normal" font="default" size="100%">Pas, Eric I.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gatsonis, Constantine</style></author><author><style face="normal" font="default" size="100%">Hodges, JamesS.</style></author><author><style face="normal" font="default" size="100%">Kass, RobertE.</style></author><author><style face="normal" font="default" size="100%">McCulloch, Robert</style></author><author><style face="normal" font="default" size="100%">Rossi, Peter</style></author><author><style face="normal" font="default" size="100%">Singpurwalla, NozerD.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Random-Effects Multinomial Probit Model of Car Ownership Choice</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Bayesian Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">car ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">longitudinal data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2290-3_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">121</style></volume><pages><style face="normal" font="default" size="100%">419-434</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-94990-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The number of cars in a household has an important effect on its travel behavior (e.g., choice of number of trips, mode to work and non-work destinations), hence car ownership modeling is an essential component of any travel demand forecasting effort. In this paper we report on a random effects multinomial probit model of car ownership level, estimated using longitudinal data collected in the Netherlands. A Bayesian approach is taken and the model is estimated by means of a modification of the Gibbs sampling with data augmentation algorithm considered by McCulloch and Rossi (1994). The modification consists in performing, after each Gibbs sampling cycle, a Metropolis step along a direction of constant likelihood. An examination of the simulation output illustrates the improved performance of the resulting sampler.&lt;/p&gt;
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