Likelihood-based sufficient dimension reduction

R. Dennis Cook, Liliana Forzani

Research output: Contribution to journalArticle

80 Scopus citations

Abstract

We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.

Original languageEnglish (US)
Pages (from-to)197-208
Number of pages12
JournalJournal of the American Statistical Association
Volume104
Issue number485
DOIs
StatePublished - Mar 1 2009

Keywords

  • Central subspace
  • Directional regression
  • Grassmann manifolds
  • Sliced average variance estimation
  • Sliced inverse regression

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