This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308727
Title: Variable Selection in High-Dimensional Distributions Using Multivariate Permutation Testing Procedures
Author(s): Megan Christina Orr*+ and Peng Liu and Dan Nettleton
Companies: Iowa State University and Iowa State University and Iowa State University
Address: 411 Welch Ave, Ames, IA, 50014,
Keywords: High-dimensional data ; variable selection ; permutation testing ; testing pairwise correlations

Multivariate permutation testing has been shown to be a powerful tool for identifying differences in high-dimensional multivariate distributions. Because some data sets may include hundreds or even thousands of variables, it is possible that the difference among these distributions may be due to a relatively small subset of variables. After identifying two differing distributions, we first split the variables into subsets that are approximately mutually independent of one another by using tests of pairwise correlations. Then we perform a multivariate permutation testing procedure on each subset to identify which subset(s) of variables are responsible for the difference in distributions. We use simulation to investigate the value of our method at identifying the "important" variables.

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