Sufficient dimension reduction in regressions across heterogeneous subpopulations

Liqiang Ni, R. Dennis Cook

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this method to regressions with qualitative predictors and developed a method, partial sliced inverse regression, under the assumption that the covariance matrices of the continuous predictors are constant across the levels of the qualitative predictor. We extend partial sliced inverse regression by removing the restrictive homogeneous covariance condition. This extension, which significantly expands the applicability of the previous methodology, is based on a new estimation method that makes use of a non-linear least squares objective function.

Original languageEnglish (US)
Pages (from-to)89-107
Number of pages19
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume68
Issue number1
DOIs
StatePublished - Feb 2006

Keywords

  • General partial sliced inverse regression
  • Partial sliced inverse regression
  • Sliced inverse regression
  • Sufficient dimension reduction

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