JSM 2004 Online Program

Abstract #300581

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Activity Number:	306
Type:	Contributed
Date/Time:	Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor:	Section on Nonparametric Statistics
Abstract - #300581
Title:	An Optimal Approach of Sufficient Dimension Reduction in Multivariate Regression
Author(s):	Jae Keun Yoo*+
Companies:	University of Minnesota, Twin Cities
Address:	1025 29th Ave. SE #F, Minneapolis, MN, 55414,
Keywords:	dimension reduction ; predictor effects ; central subspaces ; multivariate regressions ; chi-square distribution
Abstract:	Recently, Cook and Setodji (2003) developed a method for sufficient dimension reduction in multivariate regressions by estimating multivariate central mean space. Their test statistic for the dimension of the multivariate central mean subspace is a weighted sum of independent chi-square random variables. I provide an optimal version of this method in the same context. The test statistic for the optimal version has chi-square distribution. Additionally, the optimal version allows tests of predictor effects. A comparison of the two methods will be provided.

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