Abstract #300732

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JSM 2003 Abstract #300732
Activity Number: 174
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #300732
Title: Model-Free Variable Selection
Author(s): Lexin Li*+ and Dennis Cook and Christopher Nachtsheim
Companies: University of Minnesota and University of Minnesota and University of Minnesota, Minneapolis
Address: School of Statistics, Minneapolis, MN, 55455,
Keywords: variable selection ; model-free ; sufficient dimension reduction
Abstract:

In high-dimensional regression problems, variable selection can both significantly improve prediction accuracy and facilitate model interpretation. This article first distinguishes between variable selection and model selection, the two terms which are often used interchangeably in the majority of the literature. We then propose a set of novel variable selection approaches based on the theory of sufficient dimension reduction. The proposed approaches assume no model for the conditional distribution of the response given the predictors, and require no nonparametric smoothing. Simulations and empirical examples are presented to demonstrate the effectiveness of our approaches, as well as the advantages of a model-free approach in the early stages of investigation.

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