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Activity Details

296 Tue, 7/31/2012, 8:30 AM - 10:20 AM CC-Room 28A
Methods in High-Dimensional Regression — Contributed Papers
Biometrics Section , Section on Statistical Learning and Data Mining
Chair(s): Daniel Rowe, Marquette University
8:35 AM Hypothesis Testing in High-Dimensional Sparse Regression Rajarshi Mukherjee
8:50 AM Multiple Imputation for High-Dimensional Mixed Incomplete Data Using a Factor Model Ren He, University of California at Los Angeles Fielding School of Public Health ; Thomas R. Belin, University of California at Los Angeles
9:05 AM Incorporating Auxiliary Information for Improved Prediction in High-Dimensional Data Sets: An Ensemble of Shrinkage Approaches Philip S Boonstra, University of Michigan ; Bhramar Mukherjee, University of Michigan ; Jeremy Michael George Taylor, University of Michigan
9:20 AM Fused Estimators of the Central Subspace in Sufficient Dimension Reduction Xin Zhang, University of Minnesota-Twin Cities ; Dennis Cook, University of Minnesota
9:35 AM Multivariate Tests for High-Dimensional Data Guoqing Diao, George Mason University ; Bret Hanlon, University of Wisconsin-Madison ; Anand N. Vidyashankar, George Mason University
9:50 AM High-Dimensional Universal Dependence Variable Selection Hesen Peng, Amazon.com ; Tianwei Yu, Emory University ; Yun Bai, Philadelphia College of Osteopathic Medicine
10:05 AM Adaptive Nuclear-Norm Penalization in Multivariate Regression Kun Chen, Kansas State University ; Hongbo Dong, University of Wisconsin-Madison ; Kung-Sik Chan, University of Iowa

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