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April 2002 Sufficient dimensions reduction in regressions with categorical predictors
Francesca Chiaromonte, R.Dennis Cook, Bing Li
Ann. Statist. 30(2): 475-497 (April 2002). DOI: 10.1214/aos/1021379862


In this article, we describe how the theory of sufficient dimension reduction, and a well-known inference method for it (sliced inverse regression), can be extended to regression analyses involving both quantitative and categorical predictor variables. As statistics faces an increasing need for effective analysis strategies for high-dimensional data, the results we present significantly widen the applicative scope of sufficient dimension reduction and open the way for a new class of theoretical and methodological developments.


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Francesca Chiaromonte. R.Dennis Cook. Bing Li. "Sufficient dimensions reduction in regressions with categorical predictors." Ann. Statist. 30 (2) 475 - 497, April 2002.


Published: April 2002
First available in Project Euclid: 14 May 2002

zbMATH: 1012.62036
MathSciNet: MR1902896
Digital Object Identifier: 10.1214/aos/1021379862

Primary: 62G08
Secondary: 62G09 , 62H05

Keywords: central subspace , graphics , SAVE , SIR , visualization

Rights: Copyright © 2002 Institute of Mathematical Statistics

Vol.30 • No. 2 • April 2002
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