ASA Sections on:

Statistical Computing
Statistical Graphics

Events

JSM 2007

Statistical Computing and Statistical Graphics Paper Competition

Topic contributed papers

  • Improved Centroids Estimation for the Nearest Shrunken Centroid Classifier – Sijian Wang, University of Michigan; Ji Zhu, University of Michigan
  • Exploratory Model Analysis with R and GGobi – Hadley Wickham, Iowa State University
  • spBayes: An R Package for Univariate and Multivariate Hierarchical Point-Referenced Spatial Models – Andrew Finley, The University of Minnesota; Sudipto Banerjee, The University of Minnesota; Brad Carlin, The University of Minnesota
  • A Flexible Variable Selection Algorithm for the Cox Model with High-Dimensional Data – Alexander Pearson, University of Rochester; Derick R. Peterson, University of Rochester

Exploring Models Interactively

Invited papers

  • Bayesian Information Analysis – Aleks Jakulin, Columbia University; Andrew Gelman, Columbia University
  • Exploratory Model Analysis: Interactive Graphical Methods for Model Selection and Comparison – Simon Urbanek, AT&T Labs - Research
  • Grammatical Visualization of Statistical Models – Graham Wills, SPSS Inc.; Chunling Zhang, SPSS Inc.
  • Exploring Models for Clustering Data – Dianne Cook, Iowa State University

Applications of Visualization for Web 2.0

Topic contributed papers

  • An AJAX Web 2.0 Geospatial Visualization Framework – Stephen Eick, University of Illinois at Chicago
  • Statistical Graphics with Element Control in the Browser – Sven Knudsen and Stephen Kaluzny, Michael O'Connell
  • Using Web 2.0 for Statistical Software – Webster West, Texas A&M University
  • Statistical Graphics for Collaborative Environments – Daniel Rope, SPSS Inc.

Statistical Graphics for Everyday Use?

Panel

  • John Emerson, Yale University
  • Frederick Wicklin, SAS Institute Inc.
  • Leland Wilkinson, SPSS Inc.

Scagnostics

Invited - Papers

  • Scagnostics in R – Hadley Wickham, Iowa State University; Duncan Temple Lang, University of California, Davis
  • Scagnostic-Driven Autovisualization – Graham Wills, SPSS Inc.
  • Scagnostics for Projection Pursuit – Heike Hofmann, Iowa State University; Dianne Cook, Iowa State University; Hadley Wickham, Iowa State University

Statistical Methods for Graphs and Networks

Topic Contributed - Papers

  • Bayesian Self-Modeling Warping Regression – Donatello Telesca, University of Washington; Lurdes Inoue, University of Washington
  • What Is a 'Random Network'? – David Hunter, The Pennsylvania State University
  • Collective Inference for Network-Based Marketing – Shawndra Hill, University of Pennsylvania
  • Transitivity in Weighted Graphs: Effects on the Topology of Knowledge and Social Networks – Tiago Simas, Indiana University; Bharat Dravid, Indiana University; Luis Rocha, Indiana University

Statistical Graphics for Analysis of Drug Safety and Efficacy

Topic Contributed - Papers

Statistical Graphics - Methods and Applications

Contributed - Papers

  • Visualizing Cluster-Compressed Multivariable and Multialtitude Atmospheric Data – Daniel Carr, George Mason University; Amy Braverman, Jet Propulsion Laboratory
  • An Exploratory Stroll Along the Beach – Charlotte Wickham, University of California, Berkeley
  • Characterizing Multivariate Data with High-Resolution Human Faces – Dean Nelson, University of Pittsburgh at Greensburg; Joe Szurek, University of Pittsburgh at Greensburg
  • Graphs in Social Science Texts: We Can and Should Do Better – Naomi Robbins, NBR-Graphs; Joyce Robbins, Touro College
  • Longitudinal Multivariate Graphics in the Analysis of Time Management Data – Jessica M. Scott, Brigham Young University; Steven A. Wygant, Brigham Young University; Bruce Brown, Brigham Young University
  • Generating Data with Identical Statistics but Dissimilar Graphics: A Follow-Up to the Anscombe Dataset – Sangit Chatterjee, Northeastern University; Firat Aikut, Northeastern University
  • A Note on the Barnett-Cohen Censored Histogram – Jong Kim, Portland State University; Bryan G. Schar, U.S. Census Bureau

Some New Developments in Statistical Learning

Invited - Papers

  • The Adaptive Lasso and Its Oracle Properties – Hui Zou, The University of Minnesota
  • Robust Support Vector Machines – Yufeng Liu, The University of North Carolina at Chapel Hill
  • Bayesian Ensemble Active Learning – Hugh Chipman, Acadia University; Edward I. George, University of Pennsylvania; Robert McCulloch, The University of Chicago Graduate School of Business

Hardware, Software, and Algorithms

Contributed - Papers

  • WISDOM for ┬ÁStat: Web-Based Support for the Analysis of Multivariate Hierarchical Data – Knut M. Wittkowski, The Rockefeller University
  • Creating Statistical Web Services Using ASP.NET – Neil Polhemus, StatPoint, Inc.
  • Grid Computing – Abdullah Alnoshan, George Washington University; Shmuel Rotenstreich, George Washington University; Adil Rajput, BearingPoint
  • Access Control Model for E-Learning System – Fahad Bin Muhaya, Imam University; Yasmin H. Said, George Mason University
  • A Web-Based Program for Computing Percentage Points of Pearson Distributions – Wei Pan, University of Cincinnati; Haiyan Bai, University of Central Florida; Shengbao Chen, JMW Truss & Components
  • minSpline: An R Package for Fitting Splines – Sundar Dorai-Raj, PDF Solutions, Inc.; Spencer Graves, PDF Solutions, Inc.
  • Calculating the Interatomic Distance Distribution from Small-Angle X-Ray Scattering via Curve Averaging – Lanqing Hua, Purdue University; Alan Friedman, Purdue University; Chris Bailey-Kellogg, Dartmouth University; Bruce Craig, Purdue University

Symbolic, Time Series, and Image Analysis

Contributed - Papers

  • Exact Properties of a New Test and Other Tests – Jie Peng, University of Louisiana at Lafayette; Kalimuthu Krishnamoorthy, University of Louisiana at Lafayette
  • On a Moment-Based Test for Normality – Yihao Deng, Indiana University Purdue University Fort Wayne; Chand Chauhan, Indiana University Purdue University Fort Wayne
  • Testing of Hypothesis of a Structured Mean Vector for Multilevel Multivariate Data with Structured Correlations on Repeated Measurements – Anuradha Roy, The University of Texas at San Antonio; Ricardo Leiva, F.C.E. Universidad Nacional de Cuyo
  • Resampling-Based Multiple Testing Procedure – Nasrine Bendjilali, Lehigh University; Wei-Min Huang, Lehigh University
  • On an Efficient Algorithm for Boundary Detection – Tsung-Lin Cheng, National Changhua University of Education
  • A Geometric Feasible Direction Algorithm for Large-Scale Optimization with l1 Norm Constraint – Jian Zhang, Purdue University
  • Impact of Censoring on Inference for the Regression Coefficient in a Bivariate Normal Model – Richard Linder, Ohio Wesleyan University
  • Symbolic Data Analysis – Lynne Billard, University of Georgia
  • Temporal Statistics for Consequences of Alcohol Use – Peter Mburu, George Mason University; Yasmin H. Said, George Mason University; Edward Wegman, George Mason University
  • Time-Frequency Analysis of Electroencephalogram Series – Wei Yang, SUNY at Albany; Stephen Wong, University of Pennsylvania; Igor Zurbenko, SUNY at Albany
  • Temporal Extensions to Spatial Statistical Metrics – James Shine, U.S. Army Topographic Engineering Center; James P. Rogers, U.S. Army Corps of Engineers; Mete Celik, The University of Minnesota; Shashi Shekhar, The University of Minnesota
  • Approaches to Time Series Clustering – Hwanseok Choi, The University of Alabama; J. Michael Hardin, The University of Alabama
  • An Empirical Spectral Test (EST) for Random Sequences – David Zeitler, Grand Valley State University; Joseph W. McKean, Western Michigan University; John Kapenga, Western Michigan University
  • Using Geometrical Tools for Dimension Reduction of Images – Evgenia Rubinshtein, University of Central Arkansas; Anuj Srivastata, Florida State University

Large Scale Data Mining

Invited - Papers

  • A New Family of Link Functions Extending Logistic Regression – William DuMouchel, Lincoln Technologies
  • Statistics and Search Engines – Daryl Pregibon, Google
  • A Poor Man's View of Data Mining – William F. Szewczyk, National Security Agency

Mixture Models and Expectation Maximization

Contributed - Papers

  • Nonparametric Transformation of the Data to Obtain Bias Reduction in Kernel Estimation of the Distribution Function of Nonstandard Mixtures – Ennis McCune, Stephen F. Austin State University; Sandra L. McCune, Stephen F. Austin State University
  • Fitting Mixture Distributions Using Generalized Lambda Distributions (GLDs): Examples, Comparisons with Normal Mixtures, and Computational Considerations – Wei Ning, Bowling Green State University; E. J. Dudewicz, Syracuse University
  • Acceleration of the EM Reconstruction Algorithm for PET Images Using Squared Iterative Methods – Constantine E. Frangakis, Johns Hopkins University; Ravi Varadhan, Johns Hopkins University; Christophe Roland, University of Science and Technology at Lille
  • Improving the Efficiency of the Monte Carlo EM Algorithm Using Squared Iterative Methods – Ravi Varadhan, Johns Hopkins University; Brian S. Caffo, Johns Hopkins Bloomberg School of Public Health; Wolfgang Jank, University of Maryland
  • Generalized t-Copula and Its Application on Biometric – Wenmei Huang, Michigan State University; Sarat Dass, Michigan State University
  • Comparison of the Six Sigma and Lean Sigma on the IT Management Processes – Genady Grabarnik, IBM T.J. Watson Research Center; Larisa Shwartz, IBM T.J. Watson Research Center

Machine Learning

Contributed - Papers

  • Semisupervised Learning from Dissimilarity Data – Michael Trosset, Indiana University; Carey E. Priebe, Johns Hopkins University
  • Algorithms for Support Vector Machines – Denise Reeves, George Mason University
  • Random Forests for Feature Selection: To Be Handled with Caution – Carolin Strobl, LMU Munich
  • Feature Selection for Large Data – Peng Liu, Case Western Reserve University; Jiayang Sun, Case Western Reserve University
  • Asymptotic Mean Squared Prediction Error of L2Boosting Estimator Under Mis-specified Models – Tzu-Chang Cheng, University of Illinois at Urbana-Champaign; Ching-Kang Ing, Academia Sinica
  • On Efficient Supervised Learning of Multivariate t Mixture Models with Missing Information – Tsung-I Lin, National Chung Hsing University; Hsiu-J Ho, National Chung Hsing University; Pao-S Shen, Tunghai University
  • Conditional Confidence Intervals for Classification Error Rate – Hie-Choon Chung, Gwangju University; Chien-Pai Han, University of Texas at Arlington

Computationally Intensive Methods

Invited - Papers

  • Exploratory Statistical Software – Antony Unwin, University of Augsburg
  • Reducing the Variability in Least Squares Cross-Validation Bandwidths – Jeffrey Hart, Texas A&M University; Simon Sheather, Texas A&M University
  • K Models Clustering – James E. Gentle, George Mason University; Li Li, George Mason University
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