JSM 2005 - Toronto

Abstract #304450

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 268
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #304450
Title: Dimension Estimation and Variable Selection via Sufficient Dimension Reduction
Author(s): Douglas Drake*+
Companies: University of Minnesota
Address: 1622 Carl St. #103, Lauderdale, MN, 55108-1205, United States
Keywords: sufficient dimension reduction ; dimension estimation ; variable selection ; inverse regression
Abstract:

 In a regression analysis, we seek a parsimonious characterization of the conditional distribution of the response variable given the predictors. A useful part of any such characterization is a determination of which predictors are important for describing the conditional distribution. Sufficient dimension reduction is an approach to regression in which we try to replace the original predictors by their projection on a lower dimensional subspace without loss of information on the regression. I will discuss methods for consistently selecting the dimension of the subspace and the important predictors in the regression based on the inverse regression estimators of Cook and Ni (2005).

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Revised March 2005