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Abstract Details

Activity Number: 568
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305312
Title: Simultaneous Dimension Reduction and Variable Selection in Multivariate Regression
Author(s): Lisha Chen*+ and Jianhua Huang
Companies: Yale University and Texas A&M University
Address: P.O. Box 208290, New Haven, CT, 06520-8290, United States
Keywords: dimesion reduction ; variable selection ; multivariate regression

In this talk, we will introduce a new method to address the problem of predicting several response variables from the same set of predictor variables using linear regression. The method incorporates the interrelation between the response variables to improve the overall predictive accuracy. When the dimension of the predictors is high, the new proposal conducts variable selection and dimension reduction simultaneously. We will discuss the asymptotic consistency of the proposed method. The new procedure is compared with several previously proposed variable selection methods for multivariate regression and exhibits improved accuracy in prediction and variable selection.

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