JSM Preliminary Online Program
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Activity Number: 431
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #305291
Title: Dimension Folding for Array-Valued Predictors with Application to EEG Data
Author(s): Min Kyung Kim*+ and Bing Li and Naomi S. Altman
Companies: Penn State University and Penn State University and Penn State University
Address: 2605 Plaza Drive, State College, PA, 16801,
Keywords: Dimension reduction ; Dimension folding ; Kronecker envelope ; Directional Regression ; Sliced Inverse Regression ; Sliced Average Variance Estimate
Abstract:

This talk is concerned with a dimension reduction problem where the predictors are in the form of matrices or arrays. A data set that has motivated this research is the study of EEG correlates of genetic predisposition to alcoholism. This study involves two groups of subjects: an alcoholic group of 77 subjects and a control group of 45 subjects. The dimension of X, however, is almost 16,000, far exceeding the sample size n, which is 122. This types of data sets present two challenges---that they are very large and that they have are matrices or arrays. We introduce a "dimension folding" method to handle this type of data. Applying dimension folding to the EEG data set we are able to correctly identify 86 out 122 alcoholic or nonalcoholic subjects based on their EEG patterns.


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