Legend:
CC = Baltimore Convention Center,
H = Hilton Baltimore
* = applied session ! = JSM meeting theme
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 Liège ; 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