<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Benecha, H.</style></author><author><style face="normal" font="default" size="100%">Toppin, K.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Restricted Multinomial Regression for a Triple-System Estimation with List Dependence</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">BigData</style></keyword><keyword><style  face="normal" font="default" size="100%">Capture</style></keyword><keyword><style  face="normal" font="default" size="100%">DataScience</style></keyword><keyword><style  face="normal" font="default" size="100%">Dependence</style></keyword><keyword><style  face="normal" font="default" size="100%">Estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword><keyword><style  face="normal" font="default" size="100%">Probability</style></keyword><keyword><style  face="normal" font="default" size="100%">Triple-System</style></keyword><keyword><style  face="normal" font="default" size="100%">Weights</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Sartore_RestMultiReg_TSE_20170901.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The National Agricultural Statistics Service (NASS) conducts the U.S. Census of Agriculture every&amp;nbsp;five years. In 2012, NASS began using a capture-recapture approach to adjust the Census estimates&amp;nbsp;for under-coverage, non-response, and misclassification. This requires two independent samples.&amp;nbsp;NASS has kept its Census Mailing List (CML) independent from its area frame, which is used for the&amp;nbsp;June Area Survey (JAS) every June. NASS is exploring the use of web-scraping to develop a third&amp;nbsp;list-frame (TL) that would be independent of the CML and the area frame. In this paper, a Triple-System Estimation (TSE) methodology based on regularized multinomial regression is proposed to&amp;nbsp;investigate for possible dependence between the CML and the TF. A simulation study is performed&amp;nbsp;to compare the performance of the estimator based on the proposed methodology, which can take&amp;nbsp;into account the frame dependence with others already presented in the literature.&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. K. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Research access to restricted-use data</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%">2011</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">41-45</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%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">S. K. Kinney</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Risk-utility paradigms for statistical disclosure limitation: How to think, but not how to act (with discussion)</style></title><secondary-title><style face="normal" font="default" size="100%">International Statistical Review</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">79</style></volume><pages><style face="normal" font="default" size="100%">160-199</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Risk-utility formulations for problems of statistical disclosure limitation are now common. We argue that these approaches are powerful guides to official statistics agencies in regard to how to think about disclosure limitation problems, but that they fall short in essential ways from providing a sound basis for acting upon the problems. We illustrate this position in three specific contexts—transparency, tabular data and survey weights, with shorter consideration of two key emerging issues—longitudinal data and the use of administrative data to augment surveys.&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></authors></contributors><titles><title><style face="normal" font="default" size="100%">The role of transparency in statistical disclosure limitation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Joint UNECE/Eurostat Work Session on Statistical Data Confidentiality</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%">December</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2009/wp.41.e.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Bilbao, Spain</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%">Zaykin, D.V.</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%">Recursive partitioning as a tool for pharmcogenetic studies of complex diseases: II. Statistical considerations</style></title><secondary-title><style face="normal" font="default" size="100%">Pharmacogenomics</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%">77-89</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Identifying genetic variations predictive of important phenotypes, such as disease susceptibility, drug efficacy, and adverse events, remains a challenging task. There are individual polymorphisms that can be tested one at a time, but there is the more difficult problem of the identification of combinations of polymorphisms or even more complex interactions of genes with environmental factors. Diseases, drug responses or side effects can result from different mechanisms. Identification of subgroups of people where there is a common mechanism is a problem for diagnosis and prescribing of treatment. Recursive partitioning (RP) is a simple statistical tool for segmenting a population into non-overlapping groups where the response of interest, disease susceptibility, drug efficacy and adverse events are more homogeneous within the segments. We suggest that the use of RP is not only more technically feasible than other search methods but it is less susceptible to multiple-testing problems. The numbers of combinations of gene?gene and gene?environment interactions is potentially astronomical and RP greatly reduces the effective search and inference space. Moreover, the certain reliance of RP on the presence of marginal effects is justifiable as was found by using analytical and numerical arguments. In the context of haplotype analysis, results suggest that the analysis of individual SNPs is likely to be successful even when susceptibilities are determined by haplotypes. Retrospective clinical studies where cases and controls are collected will be a common design. This report provides methods that can be used to adjust the RP analysis to reflect the population incidence of the response of interest. Confidence limits on the incidence of the response in the segmented subgroups are also discussed. RP is a straightforward way to create realistic subgroups, and prediction intervals for the within-subgroup disease incidence are easily obtained.&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%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regression on distributed databases via secure multi-party computation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. dg.o 2004, National Conference on Digital Government Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><pages><style face="normal" font="default" size="100%">405-406</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jeffrey D. Picka</style></author><author><style face="normal" font="default" size="100%">Chermakani, Karthik</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Random-walk-based estimates of transport properties in small specimens of composite materials</style></title><secondary-title><style face="normal" font="default" size="100%">Phys Rev E Stat Nonlin Soft Matter Phys</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced Traveler Information Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">random walks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">4</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A method based on random walks is developed for estimating the dc conductance and similar transport properties in small specimens of composite materials. The method is valid over a much wider range of material structures than are asymptotic methods, and requires only that the internal structure of the material be known. The error in its estimates is limited primarily by CPU speed. It is found to work best for composites consisting of a bulk conducting phase and inclusions of lower conductivity.&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%">Liu L</style></author><author><style face="normal" font="default" size="100%">Hawkins DM</style></author><author><style face="normal" font="default" size="100%">Ghosh S</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%">Robust singular value decomposition analysis of microarray data</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the National Academy of Sciences</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%">100</style></volume><pages><style face="normal" font="default" size="100%">13167-13172</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%">Sun,Dongchu</style></author><author><style face="normal" font="default" size="100%">Speckman, Paul</style></author><author><style face="normal" font="default" size="100%">Tsutakawa, R. K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Random effects in generalized linear mixed models (GLMMs)</style></title><secondary-title><style face="normal" font="default" size="100%">Generalized Linear Models: A Bayesian Perspective</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Marcel dekker, Inc.</style></publisher><pages><style face="normal" font="default" size="100%">23-40</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%">RICHARD L. SMITH</style></author><author><style face="normal" font="default" size="100%">J.M. Davis</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Speckman, Paul</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%">Regression models for air pollution and daily mortality: analysis of data from Birmingham, Alabama</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%">Air Pollutants/adverse effects</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollutants/analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution/adverse effects</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution/analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Air Pollution/statistics &amp; numerical data</style></keyword><keyword><style  face="normal" font="default" size="100%">Alabama/epidemiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Mortality</style></keyword><keyword><style  face="normal" font="default" size="100%">Poisson Distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">Regression Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">719-743</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Several recent studies have reported associations between common levels of particulate air pollution and small increases in daily mortality. This study examined whether a similar association could be found in the southern United States, with different weather patterns than the previous studies, and examined the sensitivity of the results to different methods of analysis and covariate control. Data were available in Birmingham, Alabama, from August 1985 through 1988. Regression analyses controlled for weather, time trends, day of the week, and year of study and removed any long-term patterns (such as seasonal and monthly fluctuations) from the data by trigonometric filtering. A significant association was found between inhalable particles and daily mortality in Poisson regression analysis (relative risk = 1.11, 95% confidence interval 1.02-1.20). The relative risk was estimated for a 100-micrograms/m3 increase in inhalable particles. Results were unchanged when least squares regression was used, when robust regression was used, and under an alternative filtering scheme. Diagnostic plots showed that the filtering successfully removed long wavelength patterns from the data. The generalized additive model, which models the expected number of deaths as nonparametric smoothed functions of the covariates, was then used to ensure adequate control for any nonlinearities in the weather dependence. Essentially identical results for inhalable particles were seen, with no evidence of a threshold down to the lowest observed exposure levels. The association also was unchanged when all days with particulate air pollution levels in excess of the National Ambient Air Quality Standards were deleted. The magnitude of the effect is consistent with recent estimates from Philadelphia, Steubenville, Detroit, Minneapolis, St. Louis, and Utah Valley.&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%">Bar-Gera, H.</style></author><author><style face="normal" font="default" size="100%">Boyce, D. E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Route flow entropy maximization in origin-based traffic assignment, transportation and traffic theory</style></title><secondary-title><style face="normal" font="default" size="100%">14th International Symposium on Transportation and Traffic Theory</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier Science</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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boyce, D. E.</style></author><author><style face="normal" font="default" size="100%">Lee, D.-H.</style></author><author><style face="normal" font="default" size="100%">Janson, B.N.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Beckmann, Martin J.</style></author><author><style face="normal" font="default" size="100%">Johannsson, Börje</style></author><author><style face="normal" font="default" size="100%">Snickars, Folke</style></author><author><style face="normal" font="default" size="100%">Thord, Roland</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Roadway Incident Analysis with a Dynamic User-Optimal Route Choice Model</style></title><secondary-title><style face="normal" font="default" size="100%">Knowledge and Networks in a Dynamic Economy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-60318-1_21</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%">371-390</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-64350-7</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 transportation system conveys interdependencies. When analysing the costs and benefits of transport investment projects, it is therefore necessary to address the question of linkages among projects. Such linkages can occur in terms of economies of scale in arising from the combination of projects during the construction phase. Intelligent Transportation Systems (ITS), also known as Intelligent Vehicle Highway Systems (IVHS), are applying advanced technologies (such as navigation, automobile, computer science, telecommunication, electronic engineering, automatic information collection and processing) in an effort to bring revolutionary improvements in traffic safety, network capacity utilization, vehicle emission reductions, travel time and fuel consumption savings, etc. Within the framework of ITS, Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) both aim to manage and predict traffic congestion and provide historical and real time network-wide traffic information to support drivers’ route choice decisions. To enable ATMS/ATIS to achieve the above described goals, traffic flow prediction models are needed for system operation and evaluation. Linkages may also arise in supply through interaction among network components, or among the producers of transportation services. Linkages may also emerge in demand through the creation of new opportunities for interaction.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nobile, Agostino</style></author><author><style face="normal" font="default" size="100%">Bhat, Chandra R.</style></author><author><style face="normal" font="default" size="100%">Pas, Eric I.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gatsonis, Constantine</style></author><author><style face="normal" font="default" size="100%">Hodges, JamesS.</style></author><author><style face="normal" font="default" size="100%">Kass, RobertE.</style></author><author><style face="normal" font="default" size="100%">McCulloch, Robert</style></author><author><style face="normal" font="default" size="100%">Rossi, Peter</style></author><author><style face="normal" font="default" size="100%">Singpurwalla, NozerD.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Random-Effects Multinomial Probit Model of Car Ownership Choice</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Bayesian Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">car ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">longitudinal data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2290-3_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">121</style></volume><pages><style face="normal" font="default" size="100%">419-434</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-94990-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The number of cars in a household has an important effect on its travel behavior (e.g., choice of number of trips, mode to work and non-work destinations), hence car ownership modeling is an essential component of any travel demand forecasting effort. In this paper we report on a random effects multinomial probit model of car ownership level, estimated using longitudinal data collected in the Netherlands. A Bayesian approach is taken and the model is estimated by means of a modification of the Gibbs sampling with data augmentation algorithm considered by McCulloch and Rossi (1994). The modification consists in performing, after each Gibbs sampling cycle, a Metropolis step along a direction of constant likelihood. An examination of the simulation output illustrates the improved performance of the resulting sampler.&lt;/p&gt;
</style></abstract></record></records></xml>