<?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%">P. A. Rudnick</style></author><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">E. Yan</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">S. E. Stein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular &amp; Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">1341-1351</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</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%">S. K. Kinney</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">J. Miranda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving the Synthetic Longitudinal Business Database</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Journal of the IAOS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">129-135</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">2</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%">J. P. Reiter</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%">Inferentially Valid, Partially Synthetic Datasets: Generating from Predictive Distributions Not Necessary</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</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%">Fogel, P.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">Hawkins, D.M.</style></author><author><style face="normal" font="default" size="100%">Ledirac, N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferential, robust non-negative matrix factorization analysis of microarray data</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">44-49</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Motivation: Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory; even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Correlations among predictors are used to improve the statistical analysis. We exploit two ideas: non-negative matrix factorization methods that create ordered sets of predictors; and statistical testing within ordered sets which is done sequentially, removing the need for correction for multiple testing within the set. Results: Simulations and theory point to increased statistical power. Computational algorithms are described in detail. The analysis and biological interpretation of a real dataset are given. In addition to the increased power, the benefit of our method is that the organized gene lists are likely to lead better understanding of the biology. Availability: An SAS JMP executable script is available from http://www.niss.org/irMF&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">S.S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">B.E. Ankenman</style></author><author><style face="normal" font="default" size="100%">J.D. Picka</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impact of the interfacial transition zone on the chloride permeability of concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 12th Engrg. Mechanics Conf</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">1134-1137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T.L. Graves</style></author><author><style face="normal" font="default" size="100%">A. Mockus</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferring change effort from configuration management databases</style></title><secondary-title><style face="normal" font="default" size="100%">Software Metrics Symposium, 1998. Metrics 1998. Proceedings. Fifth International</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">267-273</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 this paper we describe a methodology and algorithm for historical analysis of the effort necessary for developers to make changes to software. The algorithm identifies factors which have historically increased the difficulty of changes. This methodology has implications for research into cost drivers. As an example of a research finding, we find that a system under study was “decaying” in that changes grew more difficult to implement at a rate of 20% per year. We also quantify the difference in costs between changes that fix faults and additions of new functionality: fixes require 80% more effort after accounting for size. Since our methodology adds no overhead to the development process, we also envision it being used as a project management tool: for example, developers can identify code modules which have grown more difficult to change than previously, and can match changes to developers with appropriate expertise. The methodology uses data from a change management system, supported by monthly time sheet data if available. The method’s performance does not degrade much when the quality of the time sheet data is limited. We validate our results using a survey of the developers under study: the change efforts resulting from the algorithm match the developers’ opinions. Our methodology includes a technique based on the jackknife to determine factors that contribute significantly to change effort&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%">Lawrence H. Cox</style></author><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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Introduction: Problems in Environmental Monitoring and Assessment</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_1</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%">1-4</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;The need for innovative statistical methods for modern environmental assessment is undisputed. The case studies in this book are a sampling of the broad sweep of statistical applications available in the environmental sciences, targeted to environmental monitoring and assessment. A unique feature of the applications presented here is that they are not isolated projects but were, instead, fostered under a long-term collaborative association between the U.S. Environmental Protection Agency (EPA) and the National Institute of Statistical Sciences (NISS). This institutional support resulted in a strong interdisciplinary component to the research, and common threads of statistical methodology and data analysis principles are seen across all of the projects. The case studies necessarily are detailed and technical and so this introductory chapter will give an overview of what follows and emphasize common themes that tie the projects together. Research, by its very nature, does not follow a direct path and depends on past results for the next step. This process is enriched through the collaboration of statisticians with other scientists.&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%">S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author><author><style face="normal" font="default" size="100%">T. Styer</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%">Influence of microstructure and fracture on the transport properties in cement-based materials</style></title><secondary-title><style face="normal" font="default" size="100%">Brittle Matrix Composites - International Symposium</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%">5</style></volume><pages><style face="normal" font="default" size="100%">199-220</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%">Simpson, Douglas G</style></author><author><style face="normal" font="default" size="100%">Carroll, Raymond</style></author><author><style face="normal" font="default" size="100%">Xie, Minge</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interval Censoring And Marginal Analysis In Ordinal Regression</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural Biological and Environmental Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">categorical data</style></keyword><keyword><style  face="normal" font="default" size="100%">categorical response</style></keyword><keyword><style  face="normal" font="default" size="100%">environmental statistics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</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;This paper develops methodology for regression analysis of ordinal response data subject to interval censoring. This work is motivated by the need to analyze data from multiple studies in toxicological risk assessment. Responses are scored on an ordinal severity scale, but not all responses can be scored completely. For instance, in a mortality study, information on nonfatal but adverse outcomes may be missing. In order to address possible within–study correlations we develop a generalized estimating approach to the problem, with appropriate adjustments to uncertainty statements. We develop expressions relating parameters of the implied marginal model to the parameters of a conditional model with random effects, and, in a special case, we note an interesting equivalence between conditional and marginal modeling of ordinal responses. We illustrate the methodology in an analysis of a toxicological data-base.&lt;/p&gt;
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