<?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%">Troia, G. A.</style></author><author><style face="normal" font="default" size="100%">Olinghouse, N. G.</style></author><author><style face="normal" font="default" size="100%">Wilson, J.</style></author><author><style face="normal" font="default" size="100%">Stewart, K. O.</style></author><author><style face="normal" font="default" size="100%">Mo, Y.</style></author><author><style face="normal" font="default" size="100%">Hawkins, L.</style></author><author><style face="normal" font="default" size="100%">Kopke, R.A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Common Core Writing Standards: A descriptive study of content and alignment with a sample of former state standards</style></title><secondary-title><style face="normal" font="default" size="100%">Reading Horizons</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schifeling, T.</style></author><author><style face="normal" font="default" size="100%">Cheng, C.</style></author><author><style face="normal" font="default" size="100%">Jerome Reiter</style></author><author><style face="normal" font="default" size="100%">Hillygus, D.C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accounting for nonignorable unit nonresponse and attrition in panel studies with refreshment samples</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Survey Statistics and Methodology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">18 August 2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">265–295</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Susan Abbatiello</style></author><author><style face="normal" font="default" size="100%">Birgit Schilling</style></author><author><style face="normal" font="default" size="100%">D.R. Mani</style></author><author><style face="normal" font="default" size="100%">L.I. Shilling</style></author><author><style face="normal" font="default" size="100%">S.C. Hall</style></author><author><style face="normal" font="default" size="100%">B. McLean</style></author><author><style face="normal" font="default" size="100%">M. Albetolle</style></author><author><style face="normal" font="default" size="100%">S. Allen</style></author><author><style face="normal" font="default" size="100%">M. Burgess</style></author><author><style face="normal" font="default" size="100%">M.P. Cusack</style></author><author><style face="normal" font="default" size="100%">M Gosh</style></author><author><style face="normal" font="default" size="100%">V Hedrick</style></author><author><style face="normal" font="default" size="100%">J.M. Held</style></author><author><style face="normal" font="default" size="100%">H.D. Inerowicz</style></author><author><style face="normal" font="default" size="100%">A. Jackson</style></author><author><style face="normal" font="default" size="100%">H. Keshishian</style></author><author><style face="normal" font="default" size="100%">C.R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Makowski L</style></author><author><style face="normal" font="default" size="100%">Mesri M</style></author><author><style face="normal" font="default" size="100%">Rodriguez H</style></author><author><style face="normal" font="default" size="100%">Rudnick P</style></author><author><style face="normal" font="default" size="100%">Sadowski P</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Shaddox K</style></author><author><style face="normal" font="default" size="100%">Skates SJ</style></author><author><style face="normal" font="default" size="100%">Kuhn E</style></author><author><style face="normal" font="default" size="100%">Smith D</style></author><author><style face="normal" font="default" size="100%">Whiteaker, JR</style></author><author><style face="normal" font="default" size="100%">Whitwell C</style></author><author><style face="normal" font="default" size="100%">Zhang S</style></author><author><style face="normal" font="default" size="100%">Borchers CH</style></author><author><style face="normal" font="default" size="100%">Fisher SJ</style></author><author><style face="normal" font="default" size="100%">Gibson BW</style></author><author><style face="normal" font="default" size="100%">Liebler DC</style></author><author><style face="normal" font="default" size="100%">M.J. McCoss</style></author><author><style face="normal" font="default" size="100%">Neubert TA</style></author><author><style face="normal" font="default" size="100%">Paulovich AG</style></author><author><style face="normal" font="default" size="100%">Regnier FE</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma.</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular Cell Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">2357-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data, we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to subnanogram/ml sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and interlaboratory reproducibility was &amp;lt;20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy-isotope-labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an interlaboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality control measures, enables sensitive, specific, reproducible, and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author><author><style face="normal" font="default" size="100%">M. P. Cohen</style></author><author><style face="normal" font="default" size="100%">D. Battle</style></author><author><style face="normal" font="default" size="100%">D. L. Achorn</style></author><author><style face="normal" font="default" size="100%">A. D. McKay</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Construction of replicate weights for Project TALENT</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings, Section on Survey Research Methods 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abbatiello, S.</style></author><author><style face="normal" font="default" size="100%">Feng, X.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Mani, DR</style></author><author><style face="normal" font="default" size="100%">Schilling, B</style></author><author><style face="normal" font="default" size="100%">Maclean, B</style></author><author><style face="normal" font="default" size="100%">Zimmerman, LJ</style></author><author><style face="normal" font="default" size="100%">Cusack, MP</style></author><author><style face="normal" font="default" size="100%">Hall, SC</style></author><author><style face="normal" font="default" size="100%">Addona, T</style></author><author><style face="normal" font="default" size="100%">Allen, S</style></author><author><style face="normal" font="default" size="100%">Dodder, NG</style></author><author><style face="normal" font="default" size="100%">Ghosh, M</style></author><author><style face="normal" font="default" size="100%">Held, JM</style></author><author><style face="normal" font="default" size="100%">Hedrick, V</style></author><author><style face="normal" font="default" size="100%">Inerowicz, HD</style></author><author><style face="normal" font="default" size="100%">Jackson, A</style></author><author><style face="normal" font="default" size="100%">Keshishian, H</style></author><author><style face="normal" font="default" size="100%">Kim, JW</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Riley, CP</style></author><author><style face="normal" font="default" size="100%">Rudnick, P</style></author><author><style face="normal" font="default" size="100%">Sadowski, P</style></author><author><style face="normal" font="default" size="100%">Shaddox, K</style></author><author><style face="normal" font="default" size="100%">Smith, D</style></author><author><style face="normal" font="default" size="100%">Tomazela, D</style></author><author><style face="normal" font="default" size="100%">Wahlander, A</style></author><author><style face="normal" font="default" size="100%">Waldemarson, S</style></author><author><style face="normal" font="default" size="100%">Whitwell, CA</style></author><author><style face="normal" font="default" size="100%">You, J</style></author><author><style face="normal" font="default" size="100%">Zhang, S</style></author><author><style face="normal" font="default" size="100%">Kinsinger, CR</style></author><author><style face="normal" font="default" size="100%">Mesri, M</style></author><author><style face="normal" font="default" size="100%">Rodriguez, H</style></author><author><style face="normal" font="default" size="100%">Borchers, CH</style></author><author><style face="normal" font="default" size="100%">Buck, C</style></author><author><style face="normal" font="default" size="100%">Fisher, SJ</style></author><author><style face="normal" font="default" size="100%">Gibson, BW</style></author><author><style face="normal" font="default" size="100%">Liebler, D</style></author><author><style face="normal" font="default" size="100%">Maccoss, M</style></author><author><style face="normal" font="default" size="100%">Neubert, TA</style></author><author><style face="normal" font="default" size="100%">Paulovich, A</style></author><author><style face="normal" font="default" size="100%">Regnier, F</style></author><author><style face="normal" font="default" size="100%">Skates, SJ</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Wang, M</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design, Implementation and Multisite Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS)</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular and Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">2623-2639</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation &amp;lt;0.15, peak width coefficient of variation &amp;lt;0.15, standard deviation of RT &amp;lt;0.15 min (9 s), and the RT drift &amp;lt;0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zou, Jian</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Banks, David</style></author><author><style face="normal" font="default" size="100%">Heaton, Matthew J.</style></author><author><style face="normal" font="default" size="100%">Datta, Gauri</style></author><author><style face="normal" font="default" size="100%">Lynch, James</style></author><author><style face="normal" font="default" size="100%">Vera, Francisco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian methodology for the analysis of spatial temporal surveillance data</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Analysis and Data Mining</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">conditional autoregressive process</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random field</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial statistics</style></keyword><keyword><style  face="normal" font="default" size="100%">spatio-temporal</style></keyword><keyword><style  face="normal" font="default" size="100%">Syndromic surveillance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1002/sam.10142</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">Wiley Subscription Services, Inc., A Wiley Company</style></publisher><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">194–204</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Early and accurate detection of outbreaks is one of the most important objectives of syndromic surveillance systems. We propose a general Bayesian framework for syndromic surveillance systems. The methodology incorporates Gaussian Markov random field (GMRF) and spatio-temporal conditional autoregressive (CAR) modeling. By contrast, most previous approaches have been based on only spatial or time series models. The model has appealing probabilistic representations as well as attractive statistical properties. Based on extensive simulation studies, the model is capable of capturing outbreaks rapidly, while still limiting false positives. Â© 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 194â€“204, 2012&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. J. Heaton</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Zou</style></author><author><style face="normal" font="default" size="100%">D. L. Banks</style></author><author><style face="normal" font="default" size="100%">G. Datta</style></author><author><style face="normal" font="default" size="100%">J. Lynch</style></author><author><style face="normal" font="default" size="100%">F. Vera</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A spatio-temporal absorbing state model for disease and syndromic surveillance</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">2123-2136</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good sensitivity and specificity, proper use of covariate information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve these goals, this paper proposes a spatio-temporal conditional autoregressive hidden Markov model with an absorbing state. The model performs well in both a large simulation study and in an application to influenza/pneumonia fatality 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%">Jianqiang C. Wang</style></author><author><style face="normal" font="default" size="100%">S. H. Holan</style></author><author><style face="normal" font="default" size="100%">Balgobin Nandram</style></author><author><style face="normal" font="default" size="100%">Wendy Barboza</style></author><author><style face="normal" font="default" size="100%">Criselda Toto</style></author><author><style face="normal" font="default" size="100%">Edwin Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural, Biological, and Environmental Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian hierarchical model</style></keyword><keyword><style  face="normal" font="default" size="100%">Composite estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic model</style></keyword><keyword><style  face="normal" font="default" size="100%">Forecasting Model comparison</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October 29, 2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">84-106</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Beasley CM Jr</style></author><author><style face="normal" font="default" size="100%">Benson C</style></author><author><style face="normal" font="default" size="100%">Xia JQ</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Haber H</style></author><author><style face="normal" font="default" size="100%">Mitchell MI</style></author><author><style face="normal" font="default" size="100%">Loghin C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Systematic decrements in QTc between the first and second day of contiguous daily ECG recordings under controlled conditions</style></title><secondary-title><style face="normal" font="default" size="100%">PACE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ECG</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">1116-1127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;BACKGROUND: Many thorough QT (TQT) studies use a baseline day and double delta analysis to account for potential diurnal variation in QTc. However, little is known about systematic changes in the QTc across contiguous days when normal volunteers are brought into a controlled inpatient environment.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan A. Carr</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk.</style></author><author><style face="normal" font="default" size="100%">Henry Rodriguez</style></author><author><style face="normal" font="default" size="100%">Zivana Tezak</style></author><author><style face="normal" font="default" size="100%">Mehdi Mesri</style></author><author><style face="normal" font="default" size="100%">Daniel C. Liebler</style></author><author><style face="normal" font="default" size="100%">Susan J. Fisher</style></author><author><style face="normal" font="default" size="100%">Paul Tempst</style></author><author><style face="normal" font="default" size="100%">Tara Hiltke</style></author><author><style face="normal" font="default" size="100%">Larry G. Kessler</style></author><author><style face="normal" font="default" size="100%">Christopher R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Reena Philip</style></author><author><style face="normal" font="default" size="100%">David F. Ransohoff</style></author><author><style face="normal" font="default" size="100%">Steven J. Skates</style></author><author><style face="normal" font="default" size="100%">Fred E. Regnier</style></author><author><style face="normal" font="default" size="100%">N. Leigh Anderson</style></author><author><style face="normal" font="default" size="100%">Elizabeth Mansfield</style></author><author><style face="normal" font="default" size="100%">on behalf of the Workshop Participants</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analytical Validation of Proteomic-Based Multiplex Assays: A Workshop Report by the NCI-FDA Interagency Oncology Task Force on Molecular Diagnostics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Clinical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">237-243</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Clinical proteomics has the potential to enable the early detection of cancer through the development of multiplex assays that can inform clinical decisions. However, there has been some uncertainty among translational researchers and developers as to the specific analytical measurement criteria needed to validate protein-based multiplex assays. To begin to address the causes of this uncertainty, a day-long workshop titled “Interagency Oncology Task Force Molecular Diagnostics Workshop” was held in which members of the proteomics and regulatory communities discussed many of the analytical evaluation issues that the field should address in development of protein-based multiplex assays for clinical use. This meeting report explores the issues raised at the workshop and details the recommendations that came out of the day’s discussions, such as a workshop summary discussing the analytical evaluation issues that specific proteomic technologies should address when seeking US Food and Drug Administration approval.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. H. Holan</style></author><author><style face="normal" font="default" size="100%">D. Toth</style></author><author><style face="normal" font="default" size="100%">M. A. R. Ferreira</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian multiscale multiple imputation with implications to data confidentiality</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of American Statistical Association</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">490</style></number><volume><style face="normal" font="default" size="100%">105</style></volume><pages><style face="normal" font="default" size="100%">564-577</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many scientific, sociological, and economic applications present data that are collected on multiple scales of resolution. One particular form of multiscale data arises when data are aggregated across different scales both longitudinally and by economic sector. Frequently, such datasets experience missing observations in a manner that they can be accurately imputed, while respecting the constraints imposed by the multiscale nature of the data, using the method we propose known as Bayesian multiscale multiple imputation. Our approach couples dynamic linear models with a novel imputation step based on singular normal distribution theory. Although our method is of independent interest, one important implication of such methodology is its potential effect on confidential databases protected by means of cell suppression. In order to demonstrate the proposed methodology and to assess the effectiveness of disclosure practices in longitudinal databases, we conduct a large-scale empirical study using the U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW). During the course of our empirical investigation it is determined that several of the predicted cells are within 1% accuracy, thus causing potential concerns for data confidentiality.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Bang H</style></author><author><style face="normal" font="default" size="100%">Oktay K</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cereal-induced gender selection? Most likely a multiple testing false positive</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - Royal Society B</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://rspb.royalsocietypublishing.org/content/276/1660/1211.full</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">276</style></volume><pages><style face="normal" font="default" size="100%">1211-1212</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. Chen</style></author><author><style face="normal" font="default" size="100%">L. Brandt</style></author><author><style face="normal" font="default" size="100%">V. Gregg</style></author><author><style face="normal" font="default" size="100%">R. Traunmüller</style></author><author><style face="normal" font="default" size="100%">S. Dawes</style></author><author><style face="normal" font="default" size="100%">E. Hovy</style></author><author><style face="normal" font="default" size="100%">A. Macintosh</style></author><author><style face="normal" font="default" size="100%">C. A. Larson</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Citizen access to government statistical information</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">503-529</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern electronic technologies have dramatically increased the volume of information collected and assembled by government agencies at all levels. This chapter describes digital government research aimed at keeping government data warehouses from turning into data cemeteries. The products of the research exploit modern electronic technologies in order to allow “ordinary citizens” and researchers access to government-assembled information. The goal is to help ensure that more data also means better and more useful data. Underlying the chapter are three tensions. The first is between comprehensiveness and understandability of information available to non-technically oriented “private citizens.” The second is between ensuring usefulness of detailed statistical information and protecting confidentiality of data subjects. The third tension is between the need to analyze “global” data sets and the reality that government data are distributed among both levels of government and agencies (typically, by the “domain” of data, such as education, health, or transportation).&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">25</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. L. Banks</style></author><author><style face="normal" font="default" size="100%">N. Hengartner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Social Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia of Risk Assessment IV</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">block models</style></keyword><keyword><style  face="normal" font="default" size="100%">counterterrorism</style></keyword><keyword><style  face="normal" font="default" size="100%">exponential family</style></keyword><keyword><style  face="normal" font="default" size="100%">latent space models</style></keyword><keyword><style  face="normal" font="default" size="100%">p* models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Wiley</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social networks models are a body of statistical procedures for describing relationships between agents. The term stems from initial applications that studied interactions within human communities, but the methodology is now used much more broadly and can analyze interactions among genes, proteins, nations, and websites. In the context of risk analysis, social network models have been used to describe the formation, persistence, and breakdown of terrorist cells. They also pertain to studies of organizational behavior.&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%">Fogel, P.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">Hawkins, D.M.</style></author><author><style face="normal" font="default" size="100%">Ledirac, N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferential, robust non-negative matrix factorization analysis of microarray data</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</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%">23</style></volume><pages><style face="normal" font="default" size="100%">44-49</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Motivation: Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory; even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Correlations among predictors are used to improve the statistical analysis. We exploit two ideas: non-negative matrix factorization methods that create ordered sets of predictors; and statistical testing within ordered sets which is done sequentially, removing the need for correction for multiple testing within the set. Results: Simulations and theory point to increased statistical power. Computational algorithms are described in detail. The analysis and biological interpretation of a real dataset are given. In addition to the increased power, the benefit of our method is that the organized gene lists are likely to lead better understanding of the biology. Availability: An SAS JMP executable script is available from http://www.niss.org/irMF&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%">Murali Haran</style></author><author><style face="normal" font="default" size="100%">Alan Karr</style></author><author><style face="normal" font="default" size="100%">Michael Last</style></author><author><style face="normal" font="default" size="100%">Alessandro Orso</style></author><author><style face="normal" font="default" size="100%">Adam A. Porter</style></author><author><style face="normal" font="default" size="100%">Ashish Sanil</style></author><author><style face="normal" font="default" size="100%">Sandro Fouché</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Techniques for classifying executions of deployed software to support software engineering tasks</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE TRANSACTIONS ON SOFTWARE ENGINEERING</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">287-304</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">Fogel, P.</style></author><author><style face="normal" font="default" size="100%">Hawkins, D.M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Clustering Scotch Whiskies using Non-Negative Matrix Factorization</style></title><secondary-title><style face="normal" font="default" size="100%">Q&amp;SPES News</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%">14</style></volume><pages><style face="normal" font="default" size="100%">11-13</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%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">M. Haran</style></author><author><style face="normal" font="default" size="100%">A. A. Porter</style></author><author><style face="normal" font="default" size="100%">A. Orso</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%">Applying classification techniques to remotely-collected program execution data</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ACM SIGSOFT Symposium Foundations of Software Engineering 2005</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%">ACM</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%">Jung SH</style></author><author><style face="normal" font="default" size="100%">Bang H</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sample size calculation for multiple testing in microarray data analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Biostatistics</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%">6</style></volume><pages><style face="normal" font="default" size="100%">157-169</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%">M. Haran</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%">A Model for Relating Browsing Behavior to Site Design on the World Wide Web</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of JSM 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">American Statistical Association</style></publisher><pub-location><style face="normal" font="default" size="100%">Alexandria, VA</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>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%">Hawkins, D.M.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, R.D.</style></author><author><style face="normal" font="default" size="100%">L. Liu</style></author><author><style face="normal" font="default" size="100%">Young. S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring blood spectra for signs of ovarian cancer</style></title><secondary-title><style face="normal" font="default" size="100%">Chance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">19-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Lee</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author><author><style face="normal" font="default" size="100%">J. Hernandez</style></author><author><style face="normal" font="default" size="100%">S. Karimi</style></author><author><style face="normal" font="default" size="100%">K. Litwin</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. Elmagarmid</style></author><author><style face="normal" font="default" size="100%">W. M. McIver</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Advances in Digital Government</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Kluwer</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston</style></pub-location><pages><style face="normal" font="default" size="100%">181-196</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4020-7067-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Internet provides an efficient mechanism for Federal agencies to distribute their data to the public. However, it is imperative that such data servers have built-in mechanisms to ensure that confidentiality of the data, and the privacy of individuals or establishments represented in the data, are not violated. We describe a prototype dissemination system developed for the National Agricultural Statistics Service that uses aggregation of adjacent geographical units as a confidentiality-preserving technique. We also outline a Bayesian approach to statistical analysis of the aggregated data.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">Web-based systems that disseminate information from data but preserve confidentiality</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boyce, David</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hewings, Geoffrey J.D.</style></author><author><style face="normal" font="default" size="100%">Sonis, Michael</style></author><author><style face="normal" font="default" size="100%">Boyce, David</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined Model of Interregional Commodity Flows on a Transportation Network</style></title><secondary-title><style face="normal" font="default" size="100%">Trade, Networks and Hierarchies</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Advances in Spatial Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-662-04786-6_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">29-40</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-07712-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This chapter is motivated by two ongoing research objectives of the author. The first concerns models of flows on transportation networks. Whether the subject is personal travel or freight transportation, representation of the transportation network is necessary to determine realistically interzonal/interregional travel/transportation costs. The methodological effort required to achieve such results is nontrivial, but the issues raised by such an attempt are enlightening and worthwhile. This insight is demonstrated once more by the models considered here.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M.J. Bayarri</style></author><author><style face="normal" font="default" size="100%">J. Berger</style></author><author><style face="normal" font="default" size="100%">D. Higdon</style></author><author><style face="normal" font="default" size="100%">M. Kottas</style></author><author><style face="normal" font="default" size="100%">R. Paulo</style></author><author><style face="normal" font="default" size="100%">J. Sacks</style></author><author><style face="normal" font="default" size="100%">J. Cafeo</style></author><author><style face="normal" font="default" size="100%">J. Cavendish</style></author><author><style face="normal" font="default" size="100%">C. Lin</style></author><author><style face="normal" font="default" size="100%">J. Tu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Validating Computer Models</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Foundations for Modeling and Simulation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Society for Computer Simulation</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jaeyong Lee</style></author><author><style face="normal" font="default" size="100%">Christopher Holloman</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of aggregated data in survey sampling with application to fertilizer/pesticide usage surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Res. Official Statist</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">11–6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In many cases, the public release of survey or census data at fine geographical resolution (for example, counties) may endanger the confidentiality of respondents. A strategy for such cases is to aggregate neighboring regions into larger units that satisfy confidentiality requirements. An aggregation procedure employed in a prototype system for the US National Agricultural Statistics Service is used as context to investigate the impact of aggregation on statistical properties of the data. We propose a Bayesian simulation approach for the analysis of such aggregated data. As a consequence, we are able to specify the type of additional information (such as certain sample sizes) that needs to be released in order to enable the user to perform meaningful analyses with the aggregated data.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Hernandez</style></author><author><style face="normal" font="default" size="100%">S. Karimi</style></author><author><style face="normal" font="default" size="100%">J. Lee</style></author><author><style face="normal" font="default" size="100%">K. Litwin</style></author><author><style face="normal" font="default" size="100%">A. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Disseminating information but protecting confidentiality</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Computer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">36?37</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Tsuakawa, R. K.</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Propriety of posteriors with improper priors in hierarchical linear mixed models</style></title><secondary-title><style face="normal" font="default" size="100%">Statistica Sinica</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">77-95</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Tsuakawa, R. K.</style></author><author><style face="normal" font="default" size="100%">Kim, H.</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Analysis of Mortality Rates with Disease Maps</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">2015-2035</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This article summarizes our research on estimation of age-specific and age-adjusted mortality rates for chronic obstructive pulmonary disease (COPD) for white males. Our objectives are more precise and informative displays (than previously available) of geographic variation of the age-specific mortality rates for COPD, and investigation of the relationships between the geographic variation in mortality rates and the corresponding variation in selected covariates. For a given age class, our estimates are displayed in a choropleth map of mean rates. We develop a variation map that identifies the geographical areas where inferences are reliable. Here, the variation is measured by considering a set of maps produced using samples from the posterior distribution of the population mortality rates. Finally, we describe the spatial patterns in the age-specific maps and relate these to patterns in potential explanatory covariates such as smoking rate, annual rainfall, population density, elevation, and measures of air quality.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Woodard, R.</style></author><author><style face="normal" font="default" size="100%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Z. He</style></author><author><style face="normal" font="default" size="100%">Sheriff, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A bivariate Bayes method for improving the estimates of mortality rates with a twofold conditional autoregressive model</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural Biological and Environmental Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Missouri Turkey Hunting Survey (MTHS) is a post-season mail survey conducted by the Missouri Department of Conservation to monitor and aid in the regulation of the turkey hunting season. Questionnaires are distributed after the hunting season to a simple random sample of persons who purchased permits to hunt wild turkey during the spring season. For the 1996 turkey hunting season 95,801 persons purchased hunting permits. From these individuals a simple random sample of 6,999 hunters were selected for the survey and 5,005 of these responded. The MTHS 1 Roger Woodard (E-mail: woodard@stat.missouri.edu) is a Ph.D student and Dongchu Sun (E-mail: dsun@stat.missouri.edu) is Associate Professor of Statistics, Department of Statistics, University of Missouri, Columbia, MO 65211. Zhuoqiong He (E-mail: HEZ@mail.conservation.state.mo.us) is a biometrician and Steven L. Sheri (E-mail: SHERIS@mail.conservation.state.mo.us) is a wildlife biometrics superv&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Huang, Li-Shan</style></author><author><style face="normal" font="default" size="100%">RICHARD L. SMITH</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Meteorologically-dependent trends in urban ozone</style></title><secondary-title><style face="normal" font="default" size="100%">Environmetrics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ANOVA</style></keyword><keyword><style  face="normal" font="default" size="100%">empirical Bayes</style></keyword><keyword><style  face="normal" font="default" size="100%">regression tree</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">103–118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ozone concentrations are affected by precursor emissions and by meteorological conditions. As part of a broad study to assess the effects of standards imposed by the U.S. Environmental Protection Agency (EPA), it is of interest to analyze trends in ozone after adjusting for meteorological influences. Previous papers have studied this problem for ozone data from Chicago, using a variety of regression techniques. This paper presents a different approach, in which the meteorological influence is treated nonlinearly through a regression tree. A particular advantage of this approach is that it allows us to consider different trends within the clusters produced by the regression tree analysis. The variability of trend estimates between clusters is reduced by applying an empirical Bayes adjustment. The results confirm the findings of previous authors that there is an overall downward trend in Chicago ozone values, but they also go beyond previous analyses by showing that the trend is stronger at higher levels of ozone. Copyright © 1999 John Wiley &amp;amp; Sons, Ltd.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nobile, Agostino</style></author><author><style face="normal" font="default" size="100%">Bhat, Chandra R.</style></author><author><style face="normal" font="default" size="100%">Pas, Eric I.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gatsonis, Constantine</style></author><author><style face="normal" font="default" size="100%">Hodges, JamesS.</style></author><author><style face="normal" font="default" size="100%">Kass, RobertE.</style></author><author><style face="normal" font="default" size="100%">McCulloch, Robert</style></author><author><style face="normal" font="default" size="100%">Rossi, Peter</style></author><author><style face="normal" font="default" size="100%">Singpurwalla, NozerD.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Random-Effects Multinomial Probit Model of Car Ownership Choice</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Bayesian Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">car ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">longitudinal data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2290-3_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">121</style></volume><pages><style face="normal" font="default" size="100%">419-434</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-94990-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The number of cars in a household has an important effect on its travel behavior (e.g., choice of number of trips, mode to work and non-work destinations), hence car ownership modeling is an essential component of any travel demand forecasting effort. In this paper we report on a random effects multinomial probit model of car ownership level, estimated using longitudinal data collected in the Netherlands. A Bayesian approach is taken and the model is estimated by means of a modification of the Gibbs sampling with data augmentation algorithm considered by McCulloch and Rossi (1994). The modification consists in performing, after each Gibbs sampling cycle, a Metropolis step along a direction of constant likelihood. An examination of the simulation output illustrates the improved performance of the resulting sampler.&lt;/p&gt;
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