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Activity Details


498 Wed, 8/7/2013, 8:30 AM - 10:20 AM CC-515c
Analysis of High-Dimensional Data — Contributed Papers
Section on Statistical Learning and Data Mining
Chair(s): Qiang Sun, The University of North Carolina-Chapel Hill
8:35 AM Maximum Likelihood Selection Skew-Normal Factor Analysis Beverly Gaucher, Texas A&M University
8:50 AM Variable Selection in Complex High-Dimensional Data Based on Principal Fitted Components Moumita Karmakar, University of Maryland Baltimore County ; Kofi Placid Adragni, University of Maryland, Baltimore County
9:05 AM Lasso-Type Penalized Maximum Likelihood Factor Analysis Kei Hirose, Graduate School of Engineering Science, Osaka University ; Michio Yamamoto, Osaka University
9:20 AM Model-Based Clustering for Multivariate Binary Data with Dimension Reduction Michio Yamamoto, Osaka University ; Kenichi Hayashi, Osaka University Graduate School of Medicine
9:35 AM A New Approach to Sparsity Recovery in Linear Regression Model Haolei Weng, department of Statistics, Columbia University
9:50 AM Principal Trend Analysis for Time-Course Data Yuping Zhang, The Jackson Laboratory for Genomic Medicine
10:05 AM Robust Sparse Estimation of Multi-Response Regression Xinwei Deng, Virginia Tech ; Aurelie Lozano, IBM ; Huijing Jiang, IBM T.J. Watson Research Center



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