<?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%">W. Cui</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multidimensionality in the Performance-based Online Reading Comprehension Assessment</style></title></titles><dates><year><style  face="normal" font="default" size="100%">Submitted</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%">Erciulescu A.L.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Cruze N.</style></author><author><style face="normal" font="default" size="100%">Nandram B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based County-Level Crop Estimates Incorporating Auxiliary Sources of Information</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Royal Statistical Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Journal of the Royal Statistical Society</style></publisher><volume><style face="normal" font="default" size="100%">Series A</style></volume><issue><style face="normal" font="default" size="100%">DOI 10.1111/rssa.12390</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Erciulescu A.L.</style></author><author><style face="normal" font="default" size="100%">Cruze N.B.</style></author><author><style face="normal" font="default" size="100%">Nandram B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-based County-Level Crop Estimates Incorporating Auxiliary Sources of Information</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings. Survey Research Methods Section. Alexandria, VA: American Statistical Association.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ww2.amstat.org/MembersOnly/proceedings/2016/data/assets/pdf/389784.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">3591-3605 </style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">H. J. Kim</style></author><author><style face="normal" font="default" size="100%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">Q. Wang</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple imputation of missing or faulty values under linear constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Business Economic Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">American Statistical Association</style></publisher><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">375-386</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author><author><style face="normal" font="default" size="100%">Deborah Nolan</style></author><author><style face="normal" font="default" size="100%">Keith Soper</style></author><author><style face="normal" font="default" size="100%">Cliff Spiegelman</style></author><author><style face="normal" font="default" size="100%">Linda J. Young</style></author><author><style face="normal" font="default" size="100%">Katrina L. Kelner</style></author><author><style face="normal" font="default" size="100%">Robert A. Moffitt</style></author><author><style face="normal" font="default" size="100%">Ani Thakar</style></author><author><style face="normal" font="default" size="100%">Jordan Raddick</style></author><author><style face="normal" font="default" size="100%">Edward J. Ungvarsky</style></author><author><style face="normal" font="default" size="100%">Richard W. Carlson</style></author><author><style face="normal" font="default" size="100%">Rolf Apweiler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Make research data public? - Not always so simple: A Dialogue for statisticians and science editors</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">41-50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a dialogue about policies and practicalities of requiring published research to be accompanied by publication of the research data. This dialogue carried beyond the broad issues of the advisability, the intellectual integrity, the scientific exigencies to the relevance of these issues to statistics as a discipline and the relevance of statistics, from inference to modeling to data exploration, to science and social science policies on these issues.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhou, Y-C.</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%">Marking the Ends of T-waves: Algorithms and Experts</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Biopharmaceutical Research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</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%">2</style></volume><pages><style face="normal" font="default" size="100%">359-367</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 prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval endpoints. However, selected beats of data show that the difference between two experts’ marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave. This article develops a Bayesian approach combining both slope and curvature information. We show that the difference between the automatic Bayesian marks and either of the experts’ marks is not statistically larger than the difference between two experts’ marks, thus this approach is successful in closely approximating the experts’ results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.&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%">Zhou, Y-C.</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%">Marking the Ends of T-waves: Algorithms and Experts</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics in Biopharmaceutical Research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">QT interval</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%">2</style></volume><pages><style face="normal" font="default" size="100%">359-367</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 prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval endpoints. However, selected beats of data show that the difference between two experts’ marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave. This article develops a Bayesian approach combining both slope and curvature information. We show that the difference between the automatic Bayesian marks and either of the experts’ marks is not statistically larger than the difference between two experts’ marks, thus this approach is successful in closely approximating the experts’ results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. Oganian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Masking methods that preserve positivity constraints in microdata</style></title><secondary-title><style face="normal" font="default" size="100%">J. Statist. Planning Inf.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">constraints</style></keyword><keyword><style  face="normal" font="default" size="100%">Positivity</style></keyword><keyword><style  face="normal" font="default" size="100%">SDL method</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical disclosure limitation (SDL)</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">141</style></volume><pages><style face="normal" font="default" size="100%">31-41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Statistical agencies have conflicting obligations to protect confidential information provided by respondents to surveys or censuses and to make data available for research and planning activities. When the microdata themselves are to be released, in order to achieve these conflicting objectives, statistical agencies apply statistical disclosure limitation (SDL) methods to the data, such as noise addition, swapping or microaggregation. Some of these methods do not preserve important structure and constraints in the data, such as positivity of some attributes or inequality constraints between attributes. Failure to preserve constraints is not only problematic in terms of data utility, but also may increase disclosure risk. In this paper, we describe a method for SDL that preserves both positivity of attributes and the mean vector and covariance matrix of the original data. The basis of the method is to apply multiplicative noise with the proper, data-dependent covariance structure.&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. 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>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">N. Siddiqui</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methods for Calibrating and Validating Stochastic Micro-Simulation Traffic Models</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">North Carolina State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Raleigh</style></pub-location><volume><style face="normal" font="default" size="100%">Masters</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">masters</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kitamura, Ryuichi</style></author><author><style face="normal" font="default" size="100%">Chen, Cynthia</style></author><author><style face="normal" font="default" size="100%">Pendyala, Ram M.</style></author><author><style face="normal" font="default" size="100%">Narayanan, Ravi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Micro-simulation of daily activity-travel patterns for travel demand forecasting</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">daily activity-travel patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">forecasting</style></keyword><keyword><style  face="normal" font="default" size="100%">micro-simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic travel patterns</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1023/A%3A1005259324588</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">25-51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals’ daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shaffer, Juliet Popper</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Hypothesis Testing: A Review</style></title><secondary-title><style face="normal" font="default" size="100%">Annual Review of Psychology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">561-584</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%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Marston, J.</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%">Multiworker Household Travel Demand</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automobile ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">Demographics</style></keyword><keyword><style  face="normal" font="default" size="100%">Employed</style></keyword><keyword><style  face="normal" font="default" size="100%">Highway travel</style></keyword><keyword><style  face="normal" font="default" size="100%">Households</style></keyword><keyword><style  face="normal" font="default" size="100%">Income</style></keyword><keyword><style  face="normal" font="default" size="100%">New products</style></keyword><keyword><style  face="normal" font="default" size="100%">Population density</style></keyword><keyword><style  face="normal" font="default" size="100%">Travel behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Travel surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Trip generation</style></keyword><keyword><style  face="normal" font="default" size="100%">Urban areas</style></keyword><keyword><style  face="normal" font="default" size="100%">Vehicle miles of travel</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nhts.ornl.gov/1990/doc/demographic.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Federal Highway Administration</style></publisher><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">30 p</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The purpose of this study is to examine the travel behavior and related characteristics of multiworker households (MWHs) (defined as households with at least two workers) and how they contribute to the ever-increasing demand for transportation services. On average they have incomes which exceed the national household average and often have multiple automobiles and as households they generate a considerable number of trips. The virtual dearth of previous studies of MWHs makes an overview of their characteristics and their travel behavior necessary. This study reveals that the number of MWHs has continued to grow, as has their use of highways; they are found in disproportionate numbers in low density urban areas distant from public transportation. They also have new vehicles, and drive each vehicle more miles than other households. As households, MWHs travel more than do other households. However, an individual worker’s ability and desire to travel is constrained by time factors, among others, and transportation use by MWHs, when calculated on a per worker basis, is relatively low.</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%">RICHARD L. SMITH</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multivariate Threshold Methods</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1993</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>