JSM 2004 Online Program

Abstract #300755

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Activity Number:	276
Type:	Contributed
Date/Time:	Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor:	General Methodology
Abstract - #300755
Title:	Sufficient Dimension Reduction via Hellinger Integral
Author(s):	Xiangrong Yin*+
Companies:	University of Georgia
Address:	204 Statistics Bldg., Athens, GA, 30602,
Keywords:	dimension reduction subspace ; central subspace ; regression graphics
Abstract:	Sufficient dimension reduction has been widely used in statistics, particularly in regression. We propose a general sufficient dimension reduction method that uses Hellinger integral that makes no assumption on the model or the distribution of the predictors. While this method preserves the population properties for the log likelihood methods by Yin and Cook (2003), it may simplify and improve the computations. Consistent as well as asymptotic normality of the estimator is derived. Illustrative examples are presented.

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