<?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%">Mo, Y.</style></author><author><style face="normal" font="default" size="100%">Troia, G. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Similarities and differences in constructs represented by U.S. States’ middle school writing tests and the 2007 national assessment of educational progress writing assessment</style></title><secondary-title><style face="normal" font="default" size="100%">Assessing Writing</style></secondary-title><short-title><style face="normal" font="default" size="100%">Similarities and differences in constructs represented by U.S. States’ middle school writing tests and the 2007 national assessment of educational progress writing assessment</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Assessing Writing</style></keyword><keyword><style  face="normal" font="default" size="100%">assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">writing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S1075293517300193</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">Volume 33</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Assessing Writing</style></work-type><section><style face="normal" font="default" size="100%">48–67</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Erciulescu, Andreea L.</style></author><author><style face="normal" font="default" size="100%">Cruze, Nathan B.</style></author><author><style face="normal" font="default" size="100%">Nandram, Balgobin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Small Area Estimates for End-of-Season Agricultural Quantities</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings. Survey Research Methods Section. Alexandria, VA: American Statistical Association.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/2017%20-%20Small%20Area%20Estimates%20for%20End-Of-Season%20Agricultural%20Quantities.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Fabbri, P.</style></author><author><style face="normal" font="default" size="100%">Gaetan, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">spMC: an R-package for 3D Lithological Reconstructions Based on Spatial Markov Chains</style></title><secondary-title><style face="normal" font="default" size="100%">Computers and Geosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0098300416301479</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">94</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 paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil prediction/simulation in a plain site of the NE Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, in spMC package the most known estimation/simulation methods as indicator Kriging and CoKriging were implemented, but also most advanced methods such as path methods and Bayesian procedure exploiting the maximum entropy. Because the spMC package was thought for intensive geostatistical computations, part of the code is implemented with parallel computing via the OpenMP constructs, allowing to deal with more than five lithologies, but trying to keep a computational efficiency. A final analysis of this computational efficiency&amp;nbsp;of spMC compares the prediction/simulation results using different numbers of CPU cores, considering the example data set of the case study available in the package.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">40-47</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%">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>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%">Isukapati, Isaac Kumar</style></author><author><style face="normal" font="default" size="100%">List, George F.</style></author><author><style face="normal" font="default" size="100%">Williams, Billy M</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Synthesizing route travel time distributions from segment travel time distributions</style></title><secondary-title><style face="normal" font="default" size="100%">Trans. Res. Rec.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2013</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">71–81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pauley, L.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Engel, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Studying the Reliability and Validity of Test Scores for Mathematical and Spatial Reasoning Tasks for Engineering Students</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings, American Society for Engineering Education</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</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%">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%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure statistical analysis of distributed databases, emphasizing what we don’t know</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%">2010</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">197-211</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%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical Careers in US Government Science Agencies</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">complex system models</style></keyword><keyword><style  face="normal" font="default" size="100%">engineering statistics</style></keyword><keyword><style  face="normal" font="default" size="100%">high-dimensional data</style></keyword><keyword><style  face="normal" font="default" size="100%">History of statistics</style></keyword><keyword><style  face="normal" font="default" size="100%">metrology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">443-453</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 role of statistics in those U.S. government agencies that focus on progress in science and engineering became prominent at the end of the Second World War. The success of statistics in that historical period came from the power of statistics to enable science to advance more rapidly and with great assurance in the interpretation of experimental results. Over the past three quarters of a century, technology has changed both the practice of science and the practice of statistics. However, the comparative advantage of statistics still rests in the ability to achieve greater precision with fewer errors and a deeper understanding. Examples illustrate some of the challenges that complex science now presents to statisticians, demanding both creativity and technical skills.&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%">David Banks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Special issue on dynamic models for social networks</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Math Organ Theory</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">259-260</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">259</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%">H. T. Banks</style></author><author><style face="normal" font="default" size="100%">H. K. Nguyen</style></author><author><style face="normal" font="default" size="100%">J. R. Samuels, Jr.</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sensitivity to noise variance in a social network dynamics model</style></title><secondary-title><style face="normal" font="default" size="100%">Q. Applied Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">66</style></volume><pages><style face="normal" font="default" size="100%">233-247</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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%">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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. E. Fienberg</style></author><author><style face="normal" font="default" size="100%">Y. Nardi</style></author><author><style face="normal" font="default" size="100%">A. Slavkovic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure logistic regression with distributed databases</style></title><secondary-title><style face="normal" font="default" size="100%">Bulletin of International Statistics Institute</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">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%">Zhang, N.-F.</style></author><author><style face="normal" font="default" size="100%">Strawderman, W.</style></author><author><style face="normal" font="default" size="100%">Liu, H.-k.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical analysis for multiple artifact problem in key comparisons with linear trends</style></title><secondary-title><style face="normal" font="default" size="100%">Metrologia</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational physics</style></keyword><keyword><style  face="normal" font="default" size="100%">instrumentation and measurement</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">21-26</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 statistical analysis for key comparisons with linear trends and multiple artefacts is proposed. This is an extension of a previous paper for a single artefact. The approach has the advantage that it is consistent with the no-trend case. The uncertainties for the key comparison reference value and the degrees of equivalence are also provided. As an example, the approach is applied to key comparison CCEM–K2.&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>13</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. Last</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Survey Costs: Workshop Report and White Paper</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">161</style></number><publisher><style face="normal" font="default" size="100%">National Institute of Statistical Sciences</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%">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>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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A statistical meteorologist looks at computational system models</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of 2004 Workshop on Verification &amp; Validation of Computer Models of High-consequence Engineering Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrian Dobra</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><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Software Systems for Tabular Data Releases</style></title><secondary-title><style face="normal" font="default" size="100%">Int. Journal of Uncertainty, Fuzziness and Knowledge Based Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">529-544</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Claudia Tebaldi</style></author><author><style face="normal" font="default" size="100%">Mike West</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical Analyses of Freeway Traffic Flows</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Forecasting</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">39–68</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">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%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S.S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">J.D. Picka</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">B.E. Ankenman</style></author><author><style face="normal" font="default" size="100%">P. Styer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical studies of the conductivity of concrete using ASTM C1202?94</style></title><secondary-title><style face="normal" font="default" size="100%">Concrete Science and Engineering</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%">2</style></volume><pages><style face="normal" font="default" size="100%">97-105</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%">G. Eick</style></author><author><style face="normal" font="default" size="100%">A. Mockus</style></author><author><style face="normal" font="default" size="100%">T.L. Graves</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Badilla</style></author><author><style face="normal" font="default" size="100%">F. Faulbaum</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">SoftStat ?97: Advances in Statistical Software 6</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Lucius &amp; Lucius</style></publisher><pages><style face="normal" font="default" size="100%">3-10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">Web-based text visualization</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xie, Minge</style></author><author><style face="normal" font="default" size="100%">Simpson, Douglas G</style></author><author><style face="normal" font="default" size="100%">Carroll, Raymond J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gregoire, Timothy G.</style></author><author><style face="normal" font="default" size="100%">Brillinger, David R.</style></author><author><style face="normal" font="default" size="100%">Diggle, PeterJ.</style></author><author><style face="normal" font="default" size="100%">Russek-Cohen, Estelle</style></author><author><style face="normal" font="default" size="100%">Warren, William G.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, Russell D.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Scaled Link Functions for Heterogeneous Ordinal Response Data*</style></title><secondary-title><style face="normal" font="default" size="100%">Modelling Longitudinal and Spatially Correlated Data</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aggregated observations</style></keyword><keyword><style  face="normal" font="default" size="100%">Generalized likelihood inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Marginal modeling approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Ordinal regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-0699-6_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">23-36</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-98216-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper describes a class ordinal regression models in which the link function has scale parameters that may be estimated along with the regression parameters. One motivation is to provide a plausible model for group level categorical responses. In this case a natural class of scaled link functions is obtained by treating the group level responses as threshold averages of possible correlated latent individual level variables. We find scaled link functions also arise naturally in other circumstances. Our methodology is illustrated through environmental risk assessment data where (correlated) individual level responses and group level responses are mixed.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dennis D. Cox</style></author><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author><author><style face="normal" font="default" size="100%">ENSOR, KATHERINE B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial sampling and the environment: some issues and directions</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental and Ecological Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">environmental monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">experimental design</style></keyword><keyword><style  face="normal" font="default" size="100%">kriging</style></keyword><keyword><style  face="normal" font="default" size="100%">multiphase sampling</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial statistics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1023/A%3A1018578513217</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">219-233</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Oehlert, Gary W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Shrinking a wet deposition network</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%">Monitoring network</style></keyword><keyword><style  face="normal" font="default" size="100%">network design</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial smoothing</style></keyword><keyword><style  face="normal" font="default" size="100%">trend analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">1347–1357</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Suppose that we must delete stations from a monitoring network. Which stations should be deleted if we wish the remaining network to have the smallest possible trend estimate variances? We use the spatial-temporal model described in Oehlert (1993, J. Am. Statist. Assoc., 88, 390–399), to model concentration of sulfate in wet deposition. Based on this model and three criteria, we choose good sets of candidate stations for deletion from the NADP/NTN network. We use the criteria: that the sum of 11 regional trend estimate variances be as small as possible, that the sum of local trend estimation variance be as small as possible, and that the sum of local mean estimation variance be as small as possible. Good choices of stations for deletion result in a modest increase in criteria (about 7 to 34%) for 100 stations deleted from the network, while random sets of 100 stations can increase criteria by a factor of two or more.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistics and Materials Science: Report of a Workshop</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">National Institute of Statistical Sciences</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>