Sufficient dimension reduction in regressions with categorical predictors

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)475-497
Number of pages23
JournalAnnals of Statistics
Issue number2
StatePublished - Apr 2002

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Sufficient dimension reduction in regressions with categorical predictors'. Together they form a unique fingerprint.

Cite this