<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Troia, G. A.</style></author><author><style face="normal" font="default" size="100%">Olinghouse, N. G.</style></author><author><style face="normal" font="default" size="100%">Wilson, J.</style></author><author><style face="normal" font="default" size="100%">Stewart, K. O.</style></author><author><style face="normal" font="default" size="100%">Mo, Y.</style></author><author><style face="normal" font="default" size="100%">Hawkins, L.</style></author><author><style face="normal" font="default" size="100%">Kopke, R.A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Common Core Writing Standards: A descriptive study of content and alignment with a sample of former state standards</style></title><secondary-title><style face="normal" font="default" size="100%">Reading Horizons</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Feng, X.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Xia, J-Q</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calibration using Constrained Smoothing with Application to Mass Spectrometry Data</style></title><secondary-title><style face="normal" font="default" size="100%">Biometrics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291541-0420</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">70</style></volume><pages><style face="normal" font="default" size="100%">398-408</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">398</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%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J. Lane</style></author><author><style face="normal" font="default" size="100%">V. Stodden</style></author><author><style face="normal" font="default" size="100%">H. Nissenbaum</style></author><author><style face="normal" font="default" size="100%">S. Bender</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Confidentiality and Data Access in the Use of Big Data: Theory and Practical Approaches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Cambridge University Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">Analytical frameworks for data release: A statistical view</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">I. A. Carrillo</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%">Combining cohorts in longitudinal surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Survey Methodology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Joint-randomization inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-cohort longitudinal surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Replication variance estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Rotating panel surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Superpopulation parameters</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighted Generalized Estimating Equations</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">149-182</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 question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization frame work for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation&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%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">W. Cui</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining NAEP Items into a Baseline Offline Reading Assessment</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">U. S. 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McKay</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Construction of replicate weights for Project TALENT</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings, Section on Survey Research Methods 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hughes-Oliver JM</style></author><author><style face="normal" font="default" size="100%">Brooks A</style></author><author><style face="normal" font="default" size="100%">Welch W</style></author><author><style face="normal" font="default" size="100%">Khaldei MG</style></author><author><style face="normal" font="default" size="100%">Hawkins DM</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Patil K</style></author><author><style face="normal" font="default" size="100%">Howell GW</style></author><author><style face="normal" font="default" size="100%">Ng RT</style></author><author><style face="normal" font="default" size="100%">Chu MT</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ChemModLab: A web-based cheminromates modeling laboratory</style></title><secondary-title><style face="normal" font="default" size="100%">Cheminformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">61-81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;ChemModLab, written by the ECCR @ NCSU consortium under NIH support, is a toolbox for fitting and assessing quantitative structure-activity relationships (QSARs). Its elements are: a cheminformatic front end used to supply molecular descriptors for use in modeling; a set of methods for fitting models; and methods for validating the resulting model. Compounds may be input as structures from which standard descriptors will be calculated using the freely available cheminformatic front end PowerMV; PowerMV also supports compound visualization. In addition, the user can directly input their own choices of descriptors, so the capability for comparing descriptors is effectively unlimited. The statistical methodologies comprise a comprehensive collection of approaches whose validity and utility have been accepted by experts in the fields. As far as possible, these tools are implemented in open-source software linked into the flexible R platform, giving the user the capability of applying many different QSAR modeling methods in a seamless way. As promising new QSAR methodologies emerge from the statistical and data-mining communities, they will be incorporated in the laboratory. The web site also incorporates links to public-domain data sets that can be used as test cases for proposed new modeling methods. The capabilities of ChemModLab are illustrated using a variety of biological responses, with different modeling methodologies being applied to each. These show clear differences in quality of the fitted QSAR model, and in computational requirements. The laboratory is web-based, and use is free. Researchers with new assay data, a new descriptor set, or a new modeling method may readily build QSAR models and benchmark their results against other findings. Users may also examine the diversity of the molecules identified by a QSAR model. Moreover, users have the choice of placing their data sets in a public area to facilitate communication with other researchers; or can keep them hidden to preserve confidentiality.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</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%">Current and emerging design and data analysis approaches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">APA Handbook of Educational Psychology, American Psychological Association</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>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.M.</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%">Constructing mathematical and spatial-reasoning measures 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%">2010</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%">Young SS</style></author><author><style face="normal" font="default" size="100%">Bang H</style></author><author><style face="normal" font="default" size="100%">Oktay K</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cereal-induced gender selection? 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Chen</style></author><author><style face="normal" font="default" size="100%">L. Brandt</style></author><author><style face="normal" font="default" size="100%">V. Gregg</style></author><author><style face="normal" font="default" size="100%">R. Traunmüller</style></author><author><style face="normal" font="default" size="100%">S. Dawes</style></author><author><style face="normal" font="default" size="100%">E. Hovy</style></author><author><style face="normal" font="default" size="100%">A. Macintosh</style></author><author><style face="normal" font="default" size="100%">C. A. Larson</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Citizen access to government statistical information</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">503-529</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern electronic technologies have dramatically increased the volume of information collected and assembled by government agencies at all levels. This chapter describes digital government research aimed at keeping government data warehouses from turning into data cemeteries. The products of the research exploit modern electronic technologies in order to allow “ordinary citizens” and researchers access to government-assembled information. The goal is to help ensure that more data also means better and more useful data. Underlying the chapter are three tensions. The first is between comprehensiveness and understandability of information available to non-technically oriented “private citizens.” The second is between ensuring usefulness of detailed statistical information and protecting confidentiality of data subjects. The third tension is between the need to analyze “global” data sets and the reality that government data are distributed among both levels of government and agencies (typically, by the “domain” of data, such as education, health, or transportation).&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">25</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M.J. Bayarri</style></author><author><style face="normal" font="default" size="100%">J. Berger</style></author><author><style face="normal" font="default" size="100%">Garcia-Donato, G.</style></author><author><style face="normal" font="default" size="100%">Liu, F.</style></author><author><style face="normal" font="default" size="100%">R. Paulo</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Palomo, J.</style></author><author><style face="normal" font="default" size="100%">Walsh, D.</style></author><author><style face="normal" font="default" size="100%">J. 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F. Karr</style></author><author><style face="normal" font="default" size="100%">A. Oganyan</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J. Domingo–Ferrer</style></author><author><style face="normal" font="default" size="100%">L. Franconi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combinations of SDC methods for microdata protection</style></title><secondary-title><style face="normal" font="default" size="100%">Privacy in Statistical Databases: CENEX–SDC Project International Conference, PSD 2006 Rome, Italy, December 13–15, 2006 Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Lin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calibration and Validation of Macroscopic, Deterministic Traffic Models</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boyce, David</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hewings, Geoffrey J.D.</style></author><author><style face="normal" font="default" size="100%">Sonis, Michael</style></author><author><style face="normal" font="default" size="100%">Boyce, David</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined Model of Interregional Commodity Flows on a Transportation Network</style></title><secondary-title><style face="normal" font="default" size="100%">Trade, Networks and Hierarchies</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Advances in Spatial Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-662-04786-6_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">29-40</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-07712-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This chapter is motivated by two ongoing research objectives of the author. The first concerns models of flows on transportation networks. Whether the subject is personal travel or freight transportation, representation of the transportation network is necessary to determine realistically interzonal/interregional travel/transportation costs. The methodological effort required to achieve such results is nontrivial, but the issues raised by such an attempt are enlightening and worthwhile. This insight is demonstrated once more by the models considered here.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jennifer Pittman Clarke</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">S. Stanley Young</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The construction and assessment of a statistical model for the prediction of protein assay data</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Computer Science</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%">42</style></volume><pages><style face="normal" font="default" size="100%">729-741</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 focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.&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%">Jennifer Pittman Clarke</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">S. Stanley Young</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The construction and assessment of a statistical model for the prediction of protein assay data</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Computer Science</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%">42</style></volume><pages><style face="normal" font="default" size="100%">729-741</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 focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">J. Rapoport</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined effect of cracking and water permeability of fiber-reinforced concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Concrete Under Severe Conditions, Proceedings of the Third International Conference on Concrete Under Severe Conditions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">71?78</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan Karr</style></author><author><style face="normal" font="default" size="100%">William DuMouchel</style></author><author><style face="normal" font="default" size="100%">Wen-Hua Ju</style></author><author><style face="normal" font="default" size="100%">Martin Theus</style></author><author><style face="normal" font="default" size="100%">Yehuda Vardi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer intrusion: detecting masqueraders</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Anomaly</style></keyword><keyword><style  face="normal" font="default" size="100%">Bayes</style></keyword><keyword><style  face="normal" font="default" size="100%">compression</style></keyword><keyword><style  face="normal" font="default" size="100%">computer security</style></keyword><keyword><style  face="normal" font="default" size="100%">high-orderMarkov</style></keyword><keyword><style  face="normal" font="default" size="100%">profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Unix</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">1-17</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Masqueraders in computer intrusion detection are people who use somebody else?s computer account. We investigate a number of statistical approaches for detecting masqueraders. To evaluate them, we collected UNIX command data from 50 users and then contaminated the data with masqueraders. The experiment was blinded. We show results from six methods, including two approaches from the computer science community.&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%">Graham, Jinko</style></author><author><style face="normal" font="default" size="100%">Curran, James</style></author><author><style face="normal" font="default" size="100%">Weir, Bruce</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conditional Genotypic Probabilities for Microsatellite Loci</style></title><secondary-title><style face="normal" font="default" size="100%">Genetics</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%">155</style></volume><pages><style face="normal" font="default" size="100%">1973-1980</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern forensic DNA profiles are constructed using microsatellites, short tandem repeats of 2-5 bases. In the absence of genetic data on a crime-specific subpopulation, one tool for evaluating profile evidence is the match probability. The match probability is the conditional probability that a random person would have the profile of interest given that the suspect has it and that these people are different members of the same subpopulation. One issue in evaluating the match probability is population differentiation, which can induce coancestry among subpopulation members. Forensic assessments that ignore coancestry typically overstate the strength of evidence against the suspect. Theory has been developed to account for coancestry; assumptions include a steady-state population and a mutation model in which the allelic state after a mutation event is independent of the prior state. Under these assumptions, the joint allelic probabilities within a subpopulation may be approximated by the moments of a Dirichlet distribution. We investigate the adequacy of this approximation for profiled loci that mutate according to a generalized stepwise model. Simulations suggest that the Dirichlet theory can still overstate the evidence against a suspect with a common microsatellite genotype. However, Dirichlet-based estimators were less biased than the product-rule estimator, which ignores coancestry.&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%">Valerie S. L. Williams</style></author><author><style face="normal" font="default" size="100%">Lyle V. Jones</style></author><author><style face="normal" font="default" size="100%">John W. Tukey</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Controlling error in multiple comparisons, with special attention to the national assessment of educational progress</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Educational and Behavioral Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">42–69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Three alternative procedures to adjust significance levels for multiplicity are the traditional Bonferroni technique, a sequential Bonferroni technique devel-oped by Hochberg (1988), and a sequential approach for controlling the false discovery rate proposed by Benjamini and Hochberg (1995). These procedures are illustrated and compared using examples from the National Assessment of Educational Progress (NAEP). A prominent advantage of the Benjamini and Hochberg (B-H) procedure, as demonstrated in these examples, is the greater invariance of statistical significance for given comparisons over alternative family sizes. Simulation studies show that all three procedures maintain a false discovery rate bounded above, often grossly, by ct (or c&amp;nbsp;/2). For both uncorre-lated and pairwise families of comparisons, the B-H technique is shown to have greater power than the Hochberg or Bonferroni procedures, and its power remains relatively stable as the number of comparisons becomes large, giving it an increasing advantage when many comparisons are involved. We recommend that results from NAEP State Assessments be reported using the B-H technique rather than the Bonferroni procedure. Two questions often asked about each of a set of observed comparisons are: (a) should we be confident about the direction or the sign of the corresponding underlying population comparison, and (b) for what interval of values should we be confident that it contains the value for the population comparison?&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xie, Minge</style></author><author><style face="normal" font="default" size="100%">Simpson, Douglas</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Nychka, Douglas</style></author><author><style face="normal" font="default" size="100%">Piegorsch, Walter W.</style></author><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Categorical Exposure-Response Regression Analysis of Toxicology Experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Environmental Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2226-2_7</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><volume><style face="normal" font="default" size="100%">132</style></volume><pages><style face="normal" font="default" size="100%">121-141</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-98478-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In the mid-1980s, an accident at the Union Carbide pesticides plant in Bhopal, India released the toxic gas methylisocyanate (MIC) in that densely populated region, killing more than 4000 people and injuring 500,000 others. Even today, many people in Bhopal are affected by illnesses related to that earlier exposure. This notorious industrial disaster not only forced scientists to pay greater attention to identifying and handling of hazardous chemicals but also prompted greater awareness of those common industrial products that contain hazard pollutants.&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%">Aslett, Robert</style></author><author><style face="normal" font="default" size="100%">Buck, Robert J.</style></author><author><style face="normal" font="default" size="100%">Duvall, Steven G.</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Circuit optimization via sequential computer experiments: design of an output buffer</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Royal Statistical Society: Series C</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Circuit simulator</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer code</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer model</style></keyword><keyword><style  face="normal" font="default" size="100%">Engineering design</style></keyword><keyword><style  face="normal" font="default" size="100%">Parameter design</style></keyword><keyword><style  face="normal" font="default" size="100%">Stochastic process</style></keyword><keyword><style  face="normal" font="default" size="100%">Visualization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">31-48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In electrical engineering, circuit designs are now often optimized via circuit simulation computer models. Typically, many response variables characterize the circuit’s performance. Each response is a function of many input variables, including factors that can be set in the engineering design and noise factors representing manufacturing conditions. We describe a modelling approach which is appropriate for the simulator’s deterministic input–output relationships. Non-linearities and interactions are identified without explicit assumptions about the functional form. These models lead to predictors to guide the reduction of the ranges of the designable factors in a sequence of experiments. Ultimately, the predictors are used to optimize the engineering design. We also show how a visualization of the fitted relationships facilitates an understanding of the engineering trade-offs between responses. The example used to demonstrate these methods, the design of a buffer circuit, has multiple targets for the responses, representing different trade-offs between the key performance measures.&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%">Steinberg, Laura J.</style></author><author><style face="normal" font="default" size="100%">Reckhow, Kenneth H.</style></author><author><style face="normal" font="default" size="100%">Wolpert, Robert L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of Parameters  in Mechanistic Models:  A Case Study of PCB Fate and Transport in Surface Waters</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><volume><style face="normal" font="default" size="100%">97</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Elliott, M. R.</style></author><author><style face="normal" font="default" size="100%">Raghunathan, T. E.</style></author><author><style face="normal" font="default" size="100%">Schenker, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Estimates from Multiple Surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Wiley StatsRef: Statistics Reference Online</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dual frame</style></keyword><keyword><style  face="normal" font="default" size="100%">imputation</style></keyword><keyword><style  face="normal" font="default" size="100%">missing data</style></keyword><keyword><style  face="normal" font="default" size="100%">non-probability samples</style></keyword><keyword><style  face="normal" font="default" size="100%">small-area estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighting</style></keyword></keywords><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Elliott%2C%20Raghunathan%2C%20%26%20Schenker%20for%20Wiley%20StatsRef.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Combining estimates from multiple surveys can be very useful, especially when the question of interest cannot be&amp;nbsp;addressed well by a single, existing survey. In this paper, we provide a brief review of methodology for combining&amp;nbsp;estimates, with a focus on dual frame, weighting-based, joint-modeling, missing-data, and small-area methods.&amp;nbsp;Many such methods are useful in situations outside the realm of combining estimates from surveys, such as&amp;nbsp;combining information from surveys with administrative data and combining probability-sample data with&amp;nbsp;non-probability sample, or “big” data. We also provide examples of comparability issues that must be kept in mind&amp;nbsp;when information from different sources is being combined.&lt;/p&gt;
</style></abstract><custom1><style face="normal" font="default" size="100%">&lt;p&gt;https://www.niss.org/sites/default/files/Elliott%2C%20Raghunathan%2C%20%26%20Schenker%20for%20Wiley%20StatsRef.pdf&lt;/p&gt;
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