<?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%">Schifeling, T.</style></author><author><style face="normal" font="default" size="100%">Cheng, C.</style></author><author><style face="normal" font="default" size="100%">Jerome Reiter</style></author><author><style face="normal" font="default" size="100%">Hillygus, D.C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accounting for nonignorable unit nonresponse and attrition in panel studies with refreshment samples</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Survey Statistics and Methodology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">18 August 2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">265–295</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">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%">J. P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical disclosure limitation in the presence of edit rules</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">121-138</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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></contributors><titles><title><style face="normal" font="default" size="100%">Analytical frameworks for data release: A statistical view</style></title><secondary-title><style face="normal" font="default" size="100%">Confidentiality and Data Access in the Use of Big Data: Theory and Practical Approaches. </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%">Cambridge University Press</style></publisher><pub-location><style face="normal" font="default" size="100%">New York City, NY</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>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%">R. Ferrell</style></author><author><style face="normal" font="default" size="100%">T. H. McCormick</style></author><author><style face="normal" font="default" size="100%">P. B. Ryan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big data, big results: Knowledge discovery in output from large-scale analytics</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Analysis and Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">404-412</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J. Lane</style></author><author><style face="normal" font="default" size="100%">V. Stodden</style></author><author><style face="normal" font="default" size="100%">H. Nissenbaum</style></author><author><style face="normal" font="default" size="100%">S. Bender</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Confidentiality and Data Access in the Use of Big Data: Theory and Practical Approaches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Cambridge University Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">Analytical frameworks for data release: A statistical view</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. A. Rudnick</style></author><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">E. Yan</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">S. E. Stein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular &amp; Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">1341-1351</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. K. Kinney</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">J. Miranda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving the Synthetic Longitudinal Business Database</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Journal of the IAOS</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%">30</style></volume><pages><style face="normal" font="default" size="100%">129-135</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">2</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%">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%">S. K. Kinney</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">J. Miranda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SynLBD 2.0: Improving the Synthetic Longitudinal Business Database</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Journal of the International Association for Official Statistics</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%">30</style></volume><pages><style face="normal" font="default" size="100%">129-135</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%">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%">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%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">S. K. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferentially Valid, Partially Synthetic Datasets: Generating from Predictive Distributions Not Necessary</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><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%">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%">S. K. Kinney</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">AP Reznek</style></author><author><style face="normal" font="default" size="100%">J Miranda</style></author><author><style face="normal" font="default" size="100%">R Jarmin</style></author><author><style face="normal" font="default" size="100%">JM Abowd</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database</style></title><secondary-title><style face="normal" font="default" size="100%">International Statistical Review</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">79</style></volume><pages><style face="normal" font="default" size="100%"> 362-384</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%">Stephan A. Carr</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk.</style></author><author><style face="normal" font="default" size="100%">Henry Rodriguez</style></author><author><style face="normal" font="default" size="100%">Zivana Tezak</style></author><author><style face="normal" font="default" size="100%">Mehdi Mesri</style></author><author><style face="normal" font="default" size="100%">Daniel C. Liebler</style></author><author><style face="normal" font="default" size="100%">Susan J. Fisher</style></author><author><style face="normal" font="default" size="100%">Paul Tempst</style></author><author><style face="normal" font="default" size="100%">Tara Hiltke</style></author><author><style face="normal" font="default" size="100%">Larry G. Kessler</style></author><author><style face="normal" font="default" size="100%">Christopher R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Reena Philip</style></author><author><style face="normal" font="default" size="100%">David F. Ransohoff</style></author><author><style face="normal" font="default" size="100%">Steven J. Skates</style></author><author><style face="normal" font="default" size="100%">Fred E. Regnier</style></author><author><style face="normal" font="default" size="100%">N. Leigh Anderson</style></author><author><style face="normal" font="default" size="100%">Elizabeth Mansfield</style></author><author><style face="normal" font="default" size="100%">on behalf of the Workshop Participants</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analytical Validation of Proteomic-Based Multiplex Assays: A Workshop Report by the NCI-FDA Interagency Oncology Task Force on Molecular Diagnostics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Clinical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">237-243</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Clinical proteomics has the potential to enable the early detection of cancer through the development of multiplex assays that can inform clinical decisions. However, there has been some uncertainty among translational researchers and developers as to the specific analytical measurement criteria needed to validate protein-based multiplex assays. To begin to address the causes of this uncertainty, a day-long workshop titled “Interagency Oncology Task Force Molecular Diagnostics Workshop” was held in which members of the proteomics and regulatory communities discussed many of the analytical evaluation issues that the field should address in development of protein-based multiplex assays for clinical use. This meeting report explores the issues raised at the workshop and details the recommendations that came out of the day’s discussions, such as a workshop summary discussing the analytical evaluation issues that specific proteomic technologies should address when seeking US Food and Drug Administration approval.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Privacy-preserving analysis of vertically partitioned data using secure matrix products</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">125-138</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. Oganyan</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%">Verification servers: enabling analysts to assess the quality of inferences from public use data</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Statistics and Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">1475-1482</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To protect confidentiality, statistical agencies typically alter data before releasing them to the public. Ideally, although generally not done, the agency also provides a way for secondary data analysts to assess the quality of inferences obtained with the released data. Quality measures can help secondary data analysts to identify inaccurate conclusions resulting from the disclosure limitation procedures, as well as have confidence in accurate conclusions. We propose a framework for an interactive, web-based system that analysts can query for measures of inferential quality. As we illustrate, agencies seeking to build such systems must consider the additional disclosure risks from releasing quality measures. We suggest some avenues of research on limiting these risks.&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%">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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Chen, Hsinchun</style></author><author><style face="normal" font="default" size="100%">Reid, Edna</style></author><author><style face="normal" font="default" size="100%">Sinai, Joshua</style></author><author><style face="normal" font="default" size="100%">Silke, Andrew</style></author><author><style face="normal" font="default" size="100%">Ganor, Boaz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Homeland Insecurity</style></title><secondary-title><style face="normal" font="default" size="100%">Terrorism Informatics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Integrated Series In Information Systems</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-0-387-71613-8_10</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">197-218</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-71612-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. There have also been claims that prospective datamining could be used to find the “signature” of terrorist cells embedded in larger networks. We present an overview of why the public has concerns about such activities and describe some proposals for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literatures on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of “selective revelation” and their confidentiality implications.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Ghosh</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%">Secure computation with horizontally partitioned data using adaptive regression splines</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational Statistics and Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">12</style></number><volume><style face="normal" font="default" size="100%">51</style></volume><pages><style face="normal" font="default" size="100%">5813-5820</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;When several data owners possess data on different records but the same variables, known as horizontally partitioned data, the owners can improve statistical inferences by sharing their data with each other. Often, however, the owners are unwilling or unable to share because the data are confidential or proprietary. Secure computation protocols enable the owners to compute parameter estimates for some statistical models, including linear regressions, without sharing individual records’ data. A drawback to these techniques is that the model must be specified in advance of initiating the protocol, and the usual exploratory strategies for determining good-fitting models have limited usefulness since the individual records are not shared. In this paper, we present a protocol for secure adaptive regression splines that allows for flexible, semi-automatic regression modeling. This reduces the risk of model mis-specification inherent in secure computation settings. We illustrate the protocol with air pollution data.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Rukhin, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistics in metrology: International key comparisons and interlaboratory studies</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Data Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">393-412</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C. N. Kohnen</style></author><author><style face="normal" font="default" size="100%">A. Oganyan</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A framework for evaluating the utility of data altered to protect confidentiality</style></title><secondary-title><style face="normal" font="default" size="100%">The American Statistician</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">60</style></volume><pages><style face="normal" font="default" size="100%">224-232</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Fulp, WJ</style></author><author><style face="normal" font="default" size="100%">F. Vera</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure, privacy-preserving analysis of distributed databases</style></title><secondary-title><style face="normal" font="default" size="100%">Technometrics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">133-143</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is clear value, in both industrial and government settings, derived from performing statistical analyses that, in effect, integrate data in multiple, distributed databases. However, the barriers to actually integrating the data can be substantial or even insurmountable. Corporations may be unwilling to share proprietary databases such as chemical databases held by pharmaceutical manufacturers, government agencies are subject to laws protecting confidentiality of data subjects, and even the sheer volume of the data may preclude actual data integration. In this paper, we show how tools from modern information technology?specifically, secure multiparty computation and networking?can be used to perform statistically valid analyses of distributed databases. The common characteristic of the methods we describe is that the owners share sufficient statistics computed on the local databases in a way that protects each owner from the others. That is, while each owner can calculate the ?complement ? of its contribution to the analysis, it cannot discern which other owners contributed what to that complement. Our focus is on horizontally partitioned data: the data records rather than the data attributes are spread among the owners. We present protocols for secure regression, contingency tables, maximum likelihood and Bayesian analysis. For low-risk situations, we describe a secure data integration protocol that integrates the databases but prevents owners from learning the source of data records other than their own. Finally, we outline three current research directions: a software system implementing the protocols, secure EM algorithms, and partially trusted third parties, which reduce incentives to owners not to be honest.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Remlinger KS</style></author><author><style face="normal" font="default" size="100%">Hughes-Oliver JM</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Lam RL</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical design of pools using optimal coverage and minimal collision</style></title><secondary-title><style face="normal" font="default" size="100%">Technom</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Pharmaceutical industry</style></keyword><keyword><style  face="normal" font="default" size="100%">Pooled data</style></keyword><keyword><style  face="normal" font="default" size="100%">Pooling</style></keyword><keyword><style  face="normal" font="default" size="100%">Screening</style></keyword><keyword><style  face="normal" font="default" size="100%">Throughput</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">133-143</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The screening of large chemical libraries to identify new compounds can be simplified by testing compounds in pools. Two criteria for designing pools are considered: optimal coverage of the chemical space and minimal collision between compounds. Four pooling designs are applied to a public database and evaluated by determining how well the design criteria are met and whether the methods are able to find diverse active compounds. While one pool was outstanding, all designed pools outperformed randomly designed pools.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Feng</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data dissemination and disclosure limitation in a world without microdata: A risk-utility framework for remote access analysis servers</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">163-177</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Jun Feng</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%">S. Stanley Young</style></author><author><style face="normal" font="default" size="100%">Jerome P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure analysis of distributed chemical databases without data integration</style></title><secondary-title><style face="normal" font="default" size="100%">J. Computer-Aided Molecular Design</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">November</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">9-10</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">739-747</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">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%">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%">Ashish P. Sanil</style></author><author><style face="normal" font="default" size="100%">Jerome P. Reiter</style></author><author><style face="normal" font="default" size="100%">Jerome P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure Regression on Distributed Databases</style></title><secondary-title><style face="normal" font="default" size="100%">J. Computational and Graphical Statist</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">263–279</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of integrated data without data integration</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%">2004</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">26-29</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%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Privacy preserving regression modelling via distributed computation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><pages><style face="normal" font="default" size="100%">677-682</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regression on distributed databases via secure multi-party computation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. dg.o 2004, National Conference on Digital Government Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><pages><style face="normal" font="default" size="100%">405-406</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C. N. Kohnen</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure regression for vertically partitioned, partially overlapping data</style></title><secondary-title><style face="normal" font="default" size="100%">ASA Proceedings 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Haitovsky, Yoel</style></author><author><style face="normal" font="default" size="100%">Ritov, Yaacov</style></author><author><style face="normal" font="default" size="100%">Lerche, HansRudolf</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounding Entries in Multi-way Contingency Tables Given a Set of Marginal Totals</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Statistical Inference</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Contributions to Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-57410-8_1</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Physica-Verlag HD</style></publisher><pages><style face="normal" font="default" size="100%">3-16</style></pages><isbn><style face="normal" font="default" size="100%">978-3-7908-0047-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We describe new results for sharp upper and lower bounds on the entries in multi-way tables of counts based on a set of released and possibly overlapping marginal tables. In particular, we present a generalized version of the shuttle algorithm proposed by Buzzigoli and Giusti that computes sharp integer bounds for an arbitrary set of fixed marginals. We also present two examples which illustrate the practical import of the bounds for assessing disclosure risk.&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%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Nagui M. Rouphail</style></author><author><style face="normal" font="default" size="100%">B. Brian Park</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistically-Based Validation of Computer Simulation Models in Traffic Operations and Management</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Transportation and Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced traffic management systems</style></keyword><keyword><style  face="normal" font="default" size="100%">computer simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">CORSIM</style></keyword><keyword><style  face="normal" font="default" size="100%">model validation</style></keyword><keyword><style  face="normal" font="default" size="100%">transportation policy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The process of model validation is crucial for the use of computer simulation models in transportation policy, planning, and operations. This article lays out obstacles and issues involved in performing a validation. We describe a general process that emphasizes five essential ingredients for validation: context, data, uncertainty, feedback, and prediction. We use a test bed to generate specific (and general) questions as well as to give concrete form to answers and to the methods used in providing them. The traffic simulation model CORSIM serves as the test bed; we apply it to assess signal-timing plans on a street network of Chicago. The validation process applied in the test bed demonstrates how well CORSIM can reproduce field conditions, identifies flaws in the model, and shows how well CORSIM predicts performance under new (untried) signal conditions. We find that CORSIM, though imperfect, is effective with some restrictions in evaluating signal plans on urban networks.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Byungkyu Park</style></author><author><style face="normal" font="default" size="100%">Nagui M. Rouphail</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Stochastic Signal Optimization Method Using Microsimulation</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Record</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">1748</style></volume><pages><style face="normal" font="default" size="100%">40-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A stochastic signal optimization method based on a genetic algorithm (GA-SOM) that interfaces with the microscopic simulation program CORSIM is assessed. A network in Chicago consisting of nine signalized intersections is used as an evaluation test bed. Taking CORSIM as the best representation of reality, the performance of the GA-SOM plan sets a ceiling on how good any (fixed) signal plan can be. An important aspect of this approach is its accommodations of variability. Also discussed is the robustness of an optimal plan under changes in demand. This benchmark is used to assess the best signal plan generated by TRANSYT-7F (T7F), Version 8.1, from among 12 reasonable strategies. The performance of the best T7F plan falls short of the benchmark on several counts, reflecting the need to account for variability in the highly stochastic system of traffic operations, which is not possible under the deterministic conditions intrinsic to T7F. As a sidelight, the performance of the GA-SOM plan within T7F is also computed and it is found to perform nearly as well as the optimum T7F plan.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">J. Rapoport</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined effect of cracking and water permeability of fiber-reinforced concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Concrete Under Severe Conditions, Proceedings of the Third International Conference on Concrete Under Severe Conditions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">71?78</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Park, B.</style></author><author><style face="normal" font="default" size="100%">N. M. Rouphail</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Traffic Simulation Model Validation Procedure Using CORSIM as a Test-Bed</style></title><secondary-title><style face="normal" font="default" size="100%">2001 International Symposium on Advanced Highway Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2001</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">N. Raghavan</style></author><author><style face="normal" font="default" size="100%">R. Bell</style></author><author><style face="normal" font="default" size="100%">M. Schonlau</style></author><author><style face="normal" font="default" size="100%">D. Pregibon</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Defection detection: Using online activity profiles to predict ISP customer vulnerability</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">506?515</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rouphail, N.</style></author><author><style face="normal" font="default" size="100%">Park, B.</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct Signal Timing Optimization: Strategy Development and Results</style></title><secondary-title><style face="normal" font="default" size="100%">XI Pan American Conference in Traffic and Transportation Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2000</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">19-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Park, B.</style></author><author><style face="normal" font="default" size="100%">N. M. Rouphail</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traffic Signal Offset Optimization Using Microscopic Simulation Program with Stochastic Process Model</style></title><secondary-title><style face="normal" font="default" size="100%">American Society of Civil Engineers</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Todd L. Graves</style></author><author><style face="normal" font="default" size="100%">Harrold, Mary Jean</style></author><author><style face="normal" font="default" size="100%">Kim, Jung-Min</style></author><author><style face="normal" font="default" size="100%">Adam Porter</style></author><author><style face="normal" font="default" size="100%">Rothermel, Gregg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Empirical Study of Regression Test Selection Techniques</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 20th International Conference on Software Engineering</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">ICSE ’98</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://dl.acm.org/citation.cfm?id=302163.302182</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pub-location><style face="normal" font="default" size="100%">Washington, DC, USA</style></pub-location><pages><style face="normal" font="default" size="100%">188–197</style></pages><isbn><style face="normal" font="default" size="100%">0-8186-8368-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of Parameters  in Mechanistic Models:  A Case Study of PCB Fate and Transport in Surface Waters</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">97</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nobile, Agostino</style></author><author><style face="normal" font="default" size="100%">Bhat, Chandra R.</style></author><author><style face="normal" font="default" size="100%">Pas, Eric I.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gatsonis, Constantine</style></author><author><style face="normal" font="default" size="100%">Hodges, JamesS.</style></author><author><style face="normal" font="default" size="100%">Kass, RobertE.</style></author><author><style face="normal" font="default" size="100%">McCulloch, Robert</style></author><author><style face="normal" font="default" size="100%">Rossi, Peter</style></author><author><style face="normal" font="default" size="100%">Singpurwalla, NozerD.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Random-Effects Multinomial Probit Model of Car Ownership Choice</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Bayesian Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">car ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">longitudinal data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2290-3_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">121</style></volume><pages><style face="normal" font="default" size="100%">419-434</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-94990-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The number of cars in a household has an important effect on its travel behavior (e.g., choice of number of trips, mode to work and non-work destinations), hence car ownership modeling is an essential component of any travel demand forecasting effort. In this paper we report on a random effects multinomial probit model of car ownership level, estimated using longitudinal data collected in the Netherlands. A Bayesian approach is taken and the model is estimated by means of a modification of the Gibbs sampling with data augmentation algorithm considered by McCulloch and Rossi (1994). The modification consists in performing, after each Gibbs sampling cycle, a Metropolis step along a direction of constant likelihood. An examination of the simulation output illustrates the improved performance of the resulting sampler.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xie, Minge</style></author><author><style face="normal" font="default" size="100%">Simpson, Douglas G</style></author><author><style face="normal" font="default" size="100%">Carroll, Raymond J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gregoire, Timothy G.</style></author><author><style face="normal" font="default" size="100%">Brillinger, David R.</style></author><author><style face="normal" font="default" size="100%">Diggle, PeterJ.</style></author><author><style face="normal" font="default" size="100%">Russek-Cohen, Estelle</style></author><author><style face="normal" font="default" size="100%">Warren, William G.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, Russell D.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Scaled Link Functions for Heterogeneous Ordinal Response Data*</style></title><secondary-title><style face="normal" font="default" size="100%">Modelling Longitudinal and Spatially Correlated Data</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aggregated observations</style></keyword><keyword><style  face="normal" font="default" size="100%">Generalized likelihood inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Marginal modeling approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Ordinal regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-0699-6_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">23-36</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-98216-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper describes a class ordinal regression models in which the link function has scale parameters that may be estimated along with the regression parameters. One motivation is to provide a plausible model for group level categorical responses. In this case a natural class of scaled link functions is obtained by treating the group level responses as threshold averages of possible correlated latent individual level variables. We find scaled link functions also arise naturally in other circumstances. Our methodology is illustrated through environmental risk assessment data where (correlated) individual level responses and group level responses are mixed.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bloomfield, Peter</style></author><author><style face="normal" font="default" size="100%">Royle, Andy</style></author><author><style face="normal" font="default" size="100%">Yang, Qing</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accounting for meteorological effects in measuring urban ozone levels and trends</style></title><secondary-title><style face="normal" font="default" size="100%">Atmospheric Environment</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">median polish</style></keyword><keyword><style  face="normal" font="default" size="100%">meteorological adjustment</style></keyword><keyword><style  face="normal" font="default" size="100%">nonlinear regression</style></keyword><keyword><style  face="normal" font="default" size="100%">nonparametric regression</style></keyword><keyword><style  face="normal" font="default" size="100%">Ozone concentration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">3067-3077</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Observed ozone concentrations are valuable indicators of possible health and environmental impacts. However, they are also used to monitor changes and trends in the sources of ozone and of its precursors, and for this purpose the influence of meteorological variables is a confounding factor. This paper examines ozone concentrations and meteorology in the Chicago area. The data are described using least absolute deviations and local regression. The key relationships observed in these analyses are then used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology, and to ‘adjust’ observed ozone concentrations for anomalous weather conditions.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">3067</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%">Bloomfield, Peter</style></author><author><style face="normal" font="default" size="100%">Royle, Andy</style></author><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Yang, Qing</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accounting for  Meteorological Effects in Measuring Urban Ozone Levels and Trends</style></title><secondary-title><style face="normal" font="default" size="100%">Atmospheric Environment</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">median polish</style></keyword><keyword><style  face="normal" font="default" size="100%">meteorological adjustment</style></keyword><keyword><style  face="normal" font="default" size="100%">nonlinear regression</style></keyword><keyword><style  face="normal" font="default" size="100%">nonparametric regression</style></keyword><keyword><style  face="normal" font="default" size="100%">Ozone concentration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">3067–3077</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Observed ozone concentrations are valuable indicators of possible health and environmental impacts. However, they are also used to monitor changes and trends in the sources of ozone and of its precursors, and for this purpose the influence of meteorological variables is a confounding factor. This paper examines ozone concentrations and meteorology in the Chicago area. The data are described using least absolute deviations and local regression. The key relationships observed in these analyses are then used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology, and to ‘adjust’ observed ozone concentrations for anomalous weather conditions.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">17</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Model for Fate and  Transport of Polychlorinated Biphenyl in Upper Hudson River</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Environmental Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">122</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Model for Fate and Transport of Polychlorinated Biphenyl in Upper Hudson River</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Environmental Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Hudson River</style></keyword><keyword><style  face="normal" font="default" size="100%">PCB</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation models</style></keyword><keyword><style  face="normal" font="default" size="100%">transport phenomena</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May 1996</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">341-349</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modelers of contaminant fate and transport in surface waters typically rely on literature values when selecting parameter values for mechanistic models. While the expert judgment with which these selections are made is valuable, the information contained in contaminant concentration measurements should not be ignored. In this full-scale Bayesian analysis of polychlorinated biphenyl (PCB) contamination in the upper Hudson River, these two sources of information are combined using Bayes’ theorem. A simulation model for the fate and transport of the PCBs in the upper Hudson River forms the basis of the likelihood function while the prior density is developed from literature values. The method provides estimates for the anaerobic biodegradation half-life, aerobic biodegradation plus volatilization half-life, contaminated sediment depth, and resuspension velocity of 4,400 d, 3.2 d, 0.32 m, and 0.02 m/yr, respectively. These are significantly different than values obtained with more traditional methods, and are shown to produce better predictions than those methods when used in a cross-validation study.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ran, Bin</style></author><author><style face="normal" font="default" size="100%">Boyce, David E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Inequality Models of Ideal Dynamic User-Optimal Route Choice Problems</style></title><secondary-title><style face="normal" font="default" size="100%">Dynamic Urban Transportation Network Models</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Economics and Mathematical Systems</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-662-00773-0_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">417</style></volume><pages><style face="normal" font="default" size="100%">267-290</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-58360-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this chapter, we present both route-based and link-based variational inequality models for the ideal dynamic user-optimal route choice problem. In Section 13.1, a route-time-based VI model for ideal DUO route choice is proposed. This model is the most straight-forward formulation of route-time-based, ideal DUO route choice conditions. In Section 13.2, a multi-group route-time-based VI model is developed. In this model, each group of travelers is associated with a disutility function. Thus, the route-based ideal DUO route choice conditions are defined for each group of travelers on the basis of travel disutilities instead of travel times only.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Elliott, M. R.</style></author><author><style face="normal" font="default" size="100%">Raghunathan, T. E.</style></author><author><style face="normal" font="default" size="100%">Schenker, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Estimates from Multiple Surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Wiley StatsRef: Statistics Reference Online</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dual frame</style></keyword><keyword><style  face="normal" font="default" size="100%">imputation</style></keyword><keyword><style  face="normal" font="default" size="100%">missing data</style></keyword><keyword><style  face="normal" font="default" size="100%">non-probability samples</style></keyword><keyword><style  face="normal" font="default" size="100%">small-area estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighting</style></keyword></keywords><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Elliott%2C%20Raghunathan%2C%20%26%20Schenker%20for%20Wiley%20StatsRef.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Combining estimates from multiple surveys can be very useful, especially when the question of interest cannot be&amp;nbsp;addressed well by a single, existing survey. In this paper, we provide a brief review of methodology for combining&amp;nbsp;estimates, with a focus on dual frame, weighting-based, joint-modeling, missing-data, and small-area methods.&amp;nbsp;Many such methods are useful in situations outside the realm of combining estimates from surveys, such as&amp;nbsp;combining information from surveys with administrative data and combining probability-sample data with&amp;nbsp;non-probability sample, or “big” data. We also provide examples of comparability issues that must be kept in mind&amp;nbsp;when information from different sources is being combined.&lt;/p&gt;
</style></abstract><custom1><style face="normal" font="default" size="100%">&lt;p&gt;https://www.niss.org/sites/default/files/Elliott%2C%20Raghunathan%2C%20%26%20Schenker%20for%20Wiley%20StatsRef.pdf&lt;/p&gt;
</style></custom1></record></records></xml>