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Activity Details

304 Tue, 8/1/2017, 8:30 AM - 10:20 AM CC-313
Statistical Learning: Dimension Reduction — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Vadim Zipunnikov, Johns Hopkins University
8:35 AM Supervised Dimension Reduction with Application to Driver Gene Detection Yichen Cheng
8:50 AM Sparse Principal Component Analysis with Missing Observations Seyoung Park ; Hongyu Zhao, Yale University
9:05 AM On the Similarity of Principal Components, Random Projections and Random Column Subsampling for Dimension Reduction in High-Dimensional Linear Regression Martin Slawski, George Mason Univ
9:20 AM Information Tests on Statistical Submanifolds Michael Trosset, Indiana University ; Carey E Priebe, Johns Hopkins University
9:35 AM Dimension Selection for Two-Step Linear Discriminant Analysis Ting-Li Chen, Institute of Statistical Sciences, Academia Sinica ; Yi-Heng Sun, National Taiwan University
9:50 AM Regularized Discriminant Analysis in Presence of Cellwise Contamination Stephanie Aerts, University of Li├Ęge ; Ines Wilms, KU Leuven
10:05 AM Sequential Co-Sparse Factor Regression Aditya Mishra, University of Connecticut ; Kun Chen, Department of Statistics, University of Connecticut ; Dipak K Dey, university of connecticut
 
 
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