<?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%">Kulikowich, J.M.</style></author><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Coiro, J.</style></author><author><style face="normal" font="default" size="100%">Forzani, E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Performance Characteristics of Three Formats for Assessing Internet Research Skills in Science</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%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Fabbri, P.</style></author><author><style face="normal" font="default" size="100%">Gaetan, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">spMC: an R-package for 3D Lithological Reconstructions Based on Spatial Markov Chains</style></title><secondary-title><style face="normal" font="default" size="100%">Computers and Geosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0098300416301479</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">94</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil prediction/simulation in a plain site of the NE Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, in spMC package the most known estimation/simulation methods as indicator Kriging and CoKriging were implemented, but also most advanced methods such as path methods and Bayesian procedure exploiting the maximum entropy. Because the spMC package was thought for intensive geostatistical computations, part of the code is implemented with parallel computing via the OpenMP constructs, allowing to deal with more than five lithologies, but trying to keep a computational efficiency. A final analysis of this computational efficiency&amp;nbsp;of spMC compares the prediction/simulation results using different numbers of CPU cores, considering the example data set of the case study available in the package.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">40-47</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Susan Abbatiello</style></author><author><style face="normal" font="default" size="100%">Birgit Schilling</style></author><author><style face="normal" font="default" size="100%">D.R. Mani</style></author><author><style face="normal" font="default" size="100%">L.I. Shilling</style></author><author><style face="normal" font="default" size="100%">S.C. Hall</style></author><author><style face="normal" font="default" size="100%">B. McLean</style></author><author><style face="normal" font="default" size="100%">M. Albetolle</style></author><author><style face="normal" font="default" size="100%">S. Allen</style></author><author><style face="normal" font="default" size="100%">M. Burgess</style></author><author><style face="normal" font="default" size="100%">M.P. Cusack</style></author><author><style face="normal" font="default" size="100%">M Gosh</style></author><author><style face="normal" font="default" size="100%">V Hedrick</style></author><author><style face="normal" font="default" size="100%">J.M. Held</style></author><author><style face="normal" font="default" size="100%">H.D. Inerowicz</style></author><author><style face="normal" font="default" size="100%">A. Jackson</style></author><author><style face="normal" font="default" size="100%">H. Keshishian</style></author><author><style face="normal" font="default" size="100%">C.R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Makowski L</style></author><author><style face="normal" font="default" size="100%">Mesri M</style></author><author><style face="normal" font="default" size="100%">Rodriguez H</style></author><author><style face="normal" font="default" size="100%">Rudnick P</style></author><author><style face="normal" font="default" size="100%">Sadowski P</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Shaddox K</style></author><author><style face="normal" font="default" size="100%">Skates SJ</style></author><author><style face="normal" font="default" size="100%">Kuhn E</style></author><author><style face="normal" font="default" size="100%">Smith D</style></author><author><style face="normal" font="default" size="100%">Whiteaker, JR</style></author><author><style face="normal" font="default" size="100%">Whitwell C</style></author><author><style face="normal" font="default" size="100%">Zhang S</style></author><author><style face="normal" font="default" size="100%">Borchers CH</style></author><author><style face="normal" font="default" size="100%">Fisher SJ</style></author><author><style face="normal" font="default" size="100%">Gibson BW</style></author><author><style face="normal" font="default" size="100%">Liebler DC</style></author><author><style face="normal" font="default" size="100%">M.J. McCoss</style></author><author><style face="normal" font="default" size="100%">Neubert TA</style></author><author><style face="normal" font="default" size="100%">Paulovich AG</style></author><author><style face="normal" font="default" size="100%">Regnier FE</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma.</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular Cell Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">2357-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data, we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to subnanogram/ml sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and interlaboratory reproducibility was &amp;lt;20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy-isotope-labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an interlaboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality control measures, enables sensitive, specific, reproducible, and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9</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%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">R. Ferrell</style></author><author><style face="normal" font="default" size="100%">T. H. McCormick</style></author><author><style face="normal" font="default" size="100%">P. B. Ryan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big data, big results: Knowledge discovery in output from large-scale analytics</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Analysis and Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">404-412</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%">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%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Forzani, E.</style></author><author><style face="normal" font="default" size="100%">Burlingame, C.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.</style></author><author><style face="normal" font="default" size="100%">Coiro, J.</style></author><author><style face="normal" font="default" size="100%">Kennedy, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Neuman, S. B.</style></author><author><style face="normal" font="default" size="100%">Gambrell, L.B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The New Literacies of Online Research and Comprehension: Assessing and Preparing Students for the 21st Century with Common Core State Standards</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">International Reading Association</style></publisher><pages><style face="normal" font="default" size="100%">to appear</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">to appear</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. J. Kim</style></author><author><style face="normal" font="default" size="100%">Karr Alan F</style></author><author><style face="normal" font="default" size="100%">L. H. Cox</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author><author><style face="normal" font="default" size="100%">Q. Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous Edit-Imputation for Continuous Microdata</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">189</style></number><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abbatiello, S.</style></author><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%">Mani, DR</style></author><author><style face="normal" font="default" size="100%">Schilling, B</style></author><author><style face="normal" font="default" size="100%">Maclean, B</style></author><author><style face="normal" font="default" size="100%">Zimmerman, LJ</style></author><author><style face="normal" font="default" size="100%">Cusack, MP</style></author><author><style face="normal" font="default" size="100%">Hall, SC</style></author><author><style face="normal" font="default" size="100%">Addona, T</style></author><author><style face="normal" font="default" size="100%">Allen, S</style></author><author><style face="normal" font="default" size="100%">Dodder, NG</style></author><author><style face="normal" font="default" size="100%">Ghosh, M</style></author><author><style face="normal" font="default" size="100%">Held, JM</style></author><author><style face="normal" font="default" size="100%">Hedrick, V</style></author><author><style face="normal" font="default" size="100%">Inerowicz, HD</style></author><author><style face="normal" font="default" size="100%">Jackson, A</style></author><author><style face="normal" font="default" size="100%">Keshishian, H</style></author><author><style face="normal" font="default" size="100%">Kim, JW</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Riley, CP</style></author><author><style face="normal" font="default" size="100%">Rudnick, P</style></author><author><style face="normal" font="default" size="100%">Sadowski, P</style></author><author><style face="normal" font="default" size="100%">Shaddox, K</style></author><author><style face="normal" font="default" size="100%">Smith, D</style></author><author><style face="normal" font="default" size="100%">Tomazela, D</style></author><author><style face="normal" font="default" size="100%">Wahlander, A</style></author><author><style face="normal" font="default" size="100%">Waldemarson, S</style></author><author><style face="normal" font="default" size="100%">Whitwell, CA</style></author><author><style face="normal" font="default" size="100%">You, J</style></author><author><style face="normal" font="default" size="100%">Zhang, S</style></author><author><style face="normal" font="default" size="100%">Kinsinger, CR</style></author><author><style face="normal" font="default" size="100%">Mesri, M</style></author><author><style face="normal" font="default" size="100%">Rodriguez, H</style></author><author><style face="normal" font="default" size="100%">Borchers, CH</style></author><author><style face="normal" font="default" size="100%">Buck, C</style></author><author><style face="normal" font="default" size="100%">Fisher, SJ</style></author><author><style face="normal" font="default" size="100%">Gibson, BW</style></author><author><style face="normal" font="default" size="100%">Liebler, D</style></author><author><style face="normal" font="default" size="100%">Maccoss, M</style></author><author><style face="normal" font="default" size="100%">Neubert, TA</style></author><author><style face="normal" font="default" size="100%">Paulovich, A</style></author><author><style face="normal" font="default" size="100%">Regnier, F</style></author><author><style face="normal" font="default" size="100%">Skates, SJ</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Wang, M</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design, Implementation and Multisite Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS)</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular and Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">2623-2639</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation &amp;lt;0.15, peak width coefficient of variation &amp;lt;0.15, standard deviation of RT &amp;lt;0.15 min (9 s), and the RT drift &amp;lt;0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.&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%">Xia, J.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Feng, X.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variance Component Analysis of a Multi-Site Study of Multiple Reaction Monitoring Measurements of Peptides and Proteins in Human Plasma</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS1</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">analysis of Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">blood plasma</style></keyword><keyword><style  face="normal" font="default" size="100%">experimental design</style></keyword><keyword><style  face="normal" font="default" size="100%">Instrument calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">linear regression analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">peptides</style></keyword><keyword><style  face="normal" font="default" size="100%">plasma proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">proteomic databases</style></keyword></keywords><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%">6</style></volume><pages><style face="normal" font="default" size="100%">e14590</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 the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards.&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%">Stephan A. Carr</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk.</style></author><author><style face="normal" font="default" size="100%">Henry Rodriguez</style></author><author><style face="normal" font="default" size="100%">Zivana Tezak</style></author><author><style face="normal" font="default" size="100%">Mehdi Mesri</style></author><author><style face="normal" font="default" size="100%">Daniel C. Liebler</style></author><author><style face="normal" font="default" size="100%">Susan J. Fisher</style></author><author><style face="normal" font="default" size="100%">Paul Tempst</style></author><author><style face="normal" font="default" size="100%">Tara Hiltke</style></author><author><style face="normal" font="default" size="100%">Larry G. Kessler</style></author><author><style face="normal" font="default" size="100%">Christopher R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Reena Philip</style></author><author><style face="normal" font="default" size="100%">David F. Ransohoff</style></author><author><style face="normal" font="default" size="100%">Steven J. Skates</style></author><author><style face="normal" font="default" size="100%">Fred E. Regnier</style></author><author><style face="normal" font="default" size="100%">N. Leigh Anderson</style></author><author><style face="normal" font="default" size="100%">Elizabeth Mansfield</style></author><author><style face="normal" font="default" size="100%">on behalf of the Workshop Participants</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analytical Validation of Proteomic-Based Multiplex Assays: A Workshop Report by the NCI-FDA Interagency Oncology Task Force on Molecular Diagnostics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Clinical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">237-243</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Clinical proteomics has the potential to enable the early detection of cancer through the development of multiplex assays that can inform clinical decisions. However, there has been some uncertainty among translational researchers and developers as to the specific analytical measurement criteria needed to validate protein-based multiplex assays. To begin to address the causes of this uncertainty, a day-long workshop titled “Interagency Oncology Task Force Molecular Diagnostics Workshop” was held in which members of the proteomics and regulatory communities discussed many of the analytical evaluation issues that the field should address in development of protein-based multiplex assays for clinical use. This meeting report explores the issues raised at the workshop and details the recommendations that came out of the day’s discussions, such as a workshop summary discussing the analytical evaluation issues that specific proteomic technologies should address when seeking US Food and Drug Administration approval.&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. H. Holan</style></author><author><style face="normal" font="default" size="100%">D. Toth</style></author><author><style face="normal" font="default" size="100%">M. A. R. Ferreira</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian multiscale multiple imputation with implications to data confidentiality</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of American Statistical Association</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">490</style></number><volume><style face="normal" font="default" size="100%">105</style></volume><pages><style face="normal" font="default" size="100%">564-577</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many scientific, sociological, and economic applications present data that are collected on multiple scales of resolution. One particular form of multiscale data arises when data are aggregated across different scales both longitudinally and by economic sector. Frequently, such datasets experience missing observations in a manner that they can be accurately imputed, while respecting the constraints imposed by the multiscale nature of the data, using the method we propose known as Bayesian multiscale multiple imputation. Our approach couples dynamic linear models with a novel imputation step based on singular normal distribution theory. Although our method is of independent interest, one important implication of such methodology is its potential effect on confidential databases protected by means of cell suppression. In order to demonstrate the proposed methodology and to assess the effectiveness of disclosure practices in longitudinal databases, we conduct a large-scale empirical study using the U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW). During the course of our empirical investigation it is determined that several of the predicted cells are within 1% accuracy, thus causing potential concerns for data confidentiality.&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%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.M.</style></author><author><style face="normal" font="default" size="100%">Engel, R.</style></author><author><style face="normal" font="default" size="100%">X. Wang</style></author><author><style face="normal" font="default" size="100%">Gunning, P.</style></author><author><style face="normal" font="default" size="100%">Fleming, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Psychometric and Statistical Modeling for the Study of Retention and Graduation in Undergraduate Engineering</style></title><secondary-title><style face="normal" font="default" size="100%">Social Statistics and Higher Education Conference Volume</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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fogel, P.</style></author><author><style face="normal" font="default" size="100%">Gobinet, C.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">Zugaj, D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of unmixing methods for the separation of Quantum Dot sources</style></title><secondary-title><style face="normal" font="default" size="100%">Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS ’09. First Workshop on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Bayesian positive source separation</style></keyword><keyword><style  face="normal" font="default" size="100%">BPSS</style></keyword><keyword><style  face="normal" font="default" size="100%">cadmium compounds</style></keyword><keyword><style  face="normal" font="default" size="100%">CdSe</style></keyword><keyword><style  face="normal" font="default" size="100%">consensus nonnegative matrix factorization</style></keyword><keyword><style  face="normal" font="default" size="100%">Fluorescence</style></keyword><keyword><style  face="normal" font="default" size="100%">hyperspectral images</style></keyword><keyword><style  face="normal" font="default" size="100%">Hyperspectral imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">hyperspectral system</style></keyword><keyword><style  face="normal" font="default" size="100%">ICA</style></keyword><keyword><style  face="normal" font="default" size="100%">II-VI semiconductors</style></keyword><keyword><style  face="normal" font="default" size="100%">independent component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanobioscience</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanocrystals</style></keyword><keyword><style  face="normal" font="default" size="100%">nanometer dimensions</style></keyword><keyword><style  face="normal" font="default" size="100%">NMF</style></keyword><keyword><style  face="normal" font="default" size="100%">Photonic crystals</style></keyword><keyword><style  face="normal" font="default" size="100%">Probes</style></keyword><keyword><style  face="normal" font="default" size="100%">quantum dot sources</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantum dots</style></keyword><keyword><style  face="normal" font="default" size="100%">semiconductor crystals</style></keyword><keyword><style  face="normal" font="default" size="100%">semiconductor quantum dots</style></keyword><keyword><style  face="normal" font="default" size="100%">Source separation</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial localization</style></keyword><keyword><style  face="normal" font="default" size="100%">ultraviolet spectra</style></keyword><keyword><style  face="normal" font="default" size="100%">unmixing methods</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">1-4</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-4686-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Quantum Dots (QDs) are semiconductor crystals with nanometer dimensions, which have fluorescence properties that can be adjusted through controlling their diameter. Under ultraviolet light excitation, these nanocrystals re-emit photons in the visible spectrum, with a wavelength ranging from red to blue as their size diminishes. We created an experiment to evaluate unmixing methods for hyperspectral images. The wells of a matrix [3 times 3] were filled with individual or up to three of five QDs. The matrix was imaged by a hyperspectral system (Photon Etc., Montreal, QC, CA) and a data ldquocuberdquo of 512 rows times 512 columns times 63 wavelengths was generated. For unmixing, we tested three approaches: independent component analysis (ICA), Bayesian positive source separation (BPSS) and our new consensus non-negative matrix factorization (CNFM) method. For each of these methods, we assessed the ability to separate the different sources from both spectral and spatial localization points of view. In this situation, we showed that BPSS and CNMF model estimates were very close to the original design of our experiment and were better than the ICA results. However, the time needed for the BPSS model to converge is substantially higher than CNMF. In addition, we show how the CNMF coefficients can be used to provide reasonable bounds for the number of sources, a key issue for unmixing methods, and allow for an effective segmentation of the spatial signal.&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%">Stephen E. Fienberg</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Chen, Hsinchun</style></author><author><style face="normal" font="default" size="100%">Reid, Edna</style></author><author><style face="normal" font="default" size="100%">Sinai, Joshua</style></author><author><style face="normal" font="default" size="100%">Silke, Andrew</style></author><author><style face="normal" font="default" size="100%">Ganor, Boaz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Homeland Insecurity</style></title><secondary-title><style face="normal" font="default" size="100%">Terrorism Informatics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Integrated Series In Information Systems</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-0-387-71613-8_10</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%">18</style></volume><pages><style face="normal" font="default" size="100%">197-218</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-71612-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. There have also been claims that prospective datamining could be used to find the “signature” of terrorist cells embedded in larger networks. We present an overview of why the public has concerns about such activities and describe some proposals for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literatures on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of “selective revelation” and their confidentiality implications.&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%">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. E. Fienberg</style></author><author><style face="normal" font="default" size="100%">Y. Nardi</style></author><author><style face="normal" font="default" size="100%">A. Slavkovic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure logistic regression with distributed databases</style></title><secondary-title><style face="normal" font="default" size="100%">Bulletin of International Statistics Institute</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Murali Haran</style></author><author><style face="normal" font="default" size="100%">Alan Karr</style></author><author><style face="normal" font="default" size="100%">Michael Last</style></author><author><style face="normal" font="default" size="100%">Alessandro Orso</style></author><author><style face="normal" font="default" size="100%">Adam A. Porter</style></author><author><style face="normal" font="default" size="100%">Ashish Sanil</style></author><author><style face="normal" font="default" size="100%">Sandro Fouché</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Techniques for classifying executions of deployed software to support software engineering tasks</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE TRANSACTIONS ON SOFTWARE ENGINEERING</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">287-304</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%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">Fogel, P.</style></author><author><style face="normal" font="default" size="100%">Hawkins, D.M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Clustering Scotch Whiskies using Non-Negative Matrix Factorization</style></title><secondary-title><style face="normal" font="default" size="100%">Q&amp;SPES News</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">11-13</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%">A. 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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Fulp, WJ</style></author><author><style face="normal" font="default" size="100%">F. Vera</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">X. Lin</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure, privacy-preserving analysis of distributed databases</style></title><secondary-title><style face="normal" font="default" size="100%">Technometrics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">133-143</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is clear value, in both industrial and government settings, derived from performing statistical analyses that, in effect, integrate data in multiple, distributed databases. However, the barriers to actually integrating the data can be substantial or even insurmountable. Corporations may be unwilling to share proprietary databases such as chemical databases held by pharmaceutical manufacturers, government agencies are subject to laws protecting confidentiality of data subjects, and even the sheer volume of the data may preclude actual data integration. In this paper, we show how tools from modern information technology?specifically, secure multiparty computation and networking?can be used to perform statistically valid analyses of distributed databases. The common characteristic of the methods we describe is that the owners share sufficient statistics computed on the local databases in a way that protects each owner from the others. That is, while each owner can calculate the ?complement ? of its contribution to the analysis, it cannot discern which other owners contributed what to that complement. Our focus is on horizontally partitioned data: the data records rather than the data attributes are spread among the owners. We present protocols for secure regression, contingency tables, maximum likelihood and Bayesian analysis. For low-risk situations, we describe a secure data integration protocol that integrates the databases but prevents owners from learning the source of data records other than their own. Finally, we outline three current research directions: a software system implementing the protocols, secure EM algorithms, and partially trusted third parties, which reduce incentives to owners not to be honest.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Feng</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><author><style face="normal" font="default" size="100%">Young, S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data dissemination and disclosure limitation in a world without microdata: A risk-utility framework for remote access analysis servers</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%">2005</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">163-177</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%">Liu, J.</style></author><author><style face="normal" font="default" size="100%">J. Feng</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%">PowerMV: A Software Environment for Molecular Viewing, Descriptor Generation, Data Analysis and Hit Evaluation</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</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%">45</style></volume><pages><style face="normal" font="default" size="100%">515-522</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ideally, a team of biologists, medicinal chemists and information specialists will evaluate the hits from high throughput screening. In practice, it often falls to nonmedicinal chemists to make the initial evaluation of HTS hits. Chemical genetics and high content screening both rely on screening in cells or animals where the biological target may not be known. There is a need to place active compounds into a context to suggest potential biological mechanisms. Our idea is to build an operating environment to help the biologist make the initial evaluation of HTS data. To this end the operating environment provides viewing of compound structure files, computation of basic biologically relevant chemical properties and searching against biologically annotated chemical structure databases. The benefit is to help the nonmedicinal chemist, biologist and statistician put compounds into a potentially informative biological context. Although there are several similar public and private programs used in the pharmaceutical industry to help evaluate hits, these programs are often built for computational chemists. Our program is designed for use by biologists and statisticians.&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%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Jun Feng</style></author><author><style face="normal" font="default" size="100%">Xiaodong Lin</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author><author><style face="normal" font="default" size="100%">S. Stanley Young</style></author><author><style face="normal" font="default" size="100%">Jerome P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure analysis of distributed chemical databases without data integration</style></title><secondary-title><style face="normal" font="default" size="100%">J. Computer-Aided Molecular Design</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">November</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">9-10</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">739-747</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How Large Is the World Wide Web?</style></title><secondary-title><style face="normal" font="default" size="100%">Web Dynamics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-662-10874-1_2</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%">23-43</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-07377-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There are many metrics one could consider for estimating the size of the World Wide Web, and in the present chapter we focus on size in terms of the number N of Web pages. Since a database with all the valid URLs on the Web cannot be constructed and maintained, determining N by counting is impossible. For the same reasons, estimating N by directly sampling from the Web is also infeasible. Instead of studying the Web as a whole, one can try to assess the size of the publicly indexable Web, which is the part of the Web that is considered for indexing by the major search engines. Several groups of researchers have invested considerable efforts to develop sound sampling schemes that involve submitting a number of queries to several major search engines. Lawrence and Giles [8] developed a procedure for sampling Web documents by submitting various queries to a number of search engines. We contrast their study with the one performed by Bharat and Broder [2] in November 1997. Although both experiments took place almost in the same period of time, their estimates are significantly different. In this chapter we review how the size of the indexable Web was estimated by three groups of researchers using three different statistical models: Lawrence and Giles 18, 9], Bharat and Broder [2] and Bradlow and Schmittlein 13]. Then we present a statistical framework for the analysis of data sets collected by query-based sampling, utilizing a hierarchical Bayes formulation of the Rasch model for multiple list population estimation developed in 16]. We explain why this approach seems to be in reasonable accord with the real-world constraints and thus allows us to make credible inferences about the size of the Web. We give two different methods that lead to credible estimates of the size of the Web in a reasonable amount of time and are also consistent with the real-world constraints.&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. Dobra</style></author><author><style face="normal" font="default" size="100%">S. E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounding entries in multi-way contingency tables given a set of marginal totals</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Statistical Inference, Proceedings of the Shoresh Conference 2000</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Spr</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%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Haitovsky, Yoel</style></author><author><style face="normal" font="default" size="100%">Ritov, Yaacov</style></author><author><style face="normal" font="default" size="100%">Lerche, HansRudolf</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounding Entries in Multi-way Contingency Tables Given a Set of Marginal Totals</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Statistical Inference</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Contributions to Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-57410-8_1</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Physica-Verlag HD</style></publisher><pages><style face="normal" font="default" size="100%">3-16</style></pages><isbn><style face="normal" font="default" size="100%">978-3-7908-0047-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We describe new results for sharp upper and lower bounds on the entries in multi-way tables of counts based on a set of released and possibly overlapping marginal tables. In particular, we present a generalized version of the shuttle algorithm proposed by Buzzigoli and Giusti that computes sharp integer bounds for an arbitrary set of fixed marginals. We also present two examples which illustrate the practical import of the bounds for assessing disclosure risk.&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%">Young SS</style></author><author><style face="normal" font="default" size="100%">Wang M</style></author><author><style face="normal" font="default" size="100%">Gu F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of diverse and focused combinatorial libraries using an alternating algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemistry Information and Computer 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%">43</style></volume><pages><style face="normal" font="default" size="100%">1916-1921</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%">A. Dobra</style></author><author><style face="normal" font="default" size="100%">E. A. Erosheva</style></author><author><style face="normal" font="default" size="100%">S. E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Disclosure limitation methods based on bounds for large contingency tables with application to disability data</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Conference on New Frontiers of Statistical Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">CRC Press</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%">Dobra, A.,</style></author><author><style face="normal" font="default" size="100%">Fienberg, S.E.,</style></author><author><style face="normal" font="default" size="100%">Trottini , M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing the Risk of Disclosure of Confidential Categorical Data</style></title><secondary-title><style face="normal" font="default" size="100%">Bayesian Statistics 7, Proceedings of the Seventh Valencia International Meeting on Bayesian Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Oxford Press</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%">A. Dobra</style></author><author><style face="normal" font="default" size="100%">S. E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounding entries in multi-way contingency tables given a set of marginal totals</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Conference on Foundation of Statistical Inference and Its Applications, Jerusalem</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Software Systems for Tabular Data Releases</style></title><secondary-title><style face="normal" font="default" size="100%">Int. Journal of Uncertainty, Fuzziness and Knowledge Based Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">529-544</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrian Dobra</style></author><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounds for Cell Entries in Contingency Tables Given Marginal Totals and Decomposable Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the National Academy of Sciences of the United States of America</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%">97</style></volume><pages><style face="normal" font="default" size="100%">11885-11892</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Upper and lower bounds on cell counts in cross-classifications of nonnegative counts play important roles in a number of practical problems, inclusing statistical disclosure limitation, computer tomography, mass transportation, cell suppression, and data swapping. Some features of the Frechet bounds are well known, intuitive, and regularly used by those working on disclosure limitation methods, especially those for two-dimensional tables. We previously have described a series of results relating these bounds to theory on loglinear models for cross-classified counts. This paper provides the actual theory and proofs for the special case of decomposable loglinear models and their related independence graphs. It also includes an extension linked to the structure of reducible graphs and a discussion of the relevance of other results linked to nongraphical loglinear models.&lt;/p&gt;
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