Dimension reduction for the conditional mean in regressions with categorical predictors

Bing Li, R. Dennis Cook, Francesca Chiaromonte

Research output: Contribution to journalArticle

45 Scopus citations

Abstract

Consider the regression of a response Y. on a vector of quantitative predictors X and a categorical predictor W. In this article we describe a first method for reducing the dimension of X without loss of information on the conditional mean E(Y|X, W) and without requiring a prespecified parametric model. The method, which allows for, but does not require, parametric versions of the subpopulation mean functions E(Y|X, W = w), includes a procedure for inference about the dimension of X after reduction. This work integrates previous studies on dimension reduction for the conditional mean E(Y|X) in the absence of categorical predictors and dimension reduction for the full conditional distribution of Y|(X, W). The methodology we describe may be particularly useful for constructing low-dimensional summary plots to aid in model-building at the outset of an analysis. Our proposals provide an often parsimonious alternative to the standard technique of modeling with interaction terms to adapt a mean function for different subpopulations determined by the levels of W. Examples illustrating this and other aspects of the development are presented.

Original languageEnglish (US)
Pages (from-to)1636-1668
Number of pages33
JournalAnnals of Statistics
Volume31
Issue number5
DOIs
StatePublished - Oct 1 2003

Keywords

  • Analysis of covariance
  • Central subspace
  • Graphics
  • OLS
  • PHD
  • SAVE
  • SIR

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