JSM 2004 - Toronto

Abstract #300873

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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 - #300873
Title: Optimal Sufficient Dimension Reduction in Regressions with Categorical Predictors
Author(s): Xuerong Wen*+
Companies: University of Minnesota
Address: 313 Ford Hall, 224 Church St. SE, Minneapolis, MN, 55108,
Keywords: sufficient dimension reduction ; categorical predictors ; minimum discrepancy approach ; testing coordinates hypotheses
Abstract:

In the context of sufficient dimension reduction, a Minimum Discrepancy Approach (MDA) is introduced for regressions with a mix of continuous and categorical predictors. It is shown that current methods are special cases of this approach and an optimal method, partial inverse regression estimation (PIRE) is proposed. PIRE provides an optimal estimate of the partial central subspace (PCS). An asymptotic chi-squared distribution for rank test is given. A chi-squared test of the conditional independence hypothesis that the response is independent of a selected subset of quantitative predictors given the remaining predictors is obtained.

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