Abstract #300372


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JSM 2002 Abstract #300372
Activity Number: 86
Type: Invited
Date/Time: Monday, August 12, 2002 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract - #300372
Title: Extending Dimension Reduction to Regressions with Categorical Predictors
Author(s): Francesca Chiaromonte*+
Affiliation(s): Pennsylvania State University
Address: 411 Thomas Building, University Park, Pennsylvania, 16802,
Keywords: regression ; categorical predictors ; sufficient dimension reduction
Abstract:

As high-dimensional data sets become increasingly common, sufficient dimension reduction provides a theoretical foundation and effective methodology for the low-dimensional management of regressions involving a large number of quantitative predictors. In many applications, though, quantitative predictors, to be reduced through linear combinations, come together with qualitative predictors. In this talk, we lay down the theoretical basis of partial dimension reduction, in which the sufficient reduction of a set of quantitative predictor is filtered through a subpopulation structure generated by categorical variables. We also describe how one of the most popular dimension reduction methods, sliced inverse regression (SIR), can be adapted to partial reduction under appropriate assumptions. Finally, we present some applications.


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