Using intraslice covariances for improved estimation of the central subspace in regression

R. Dennis Cook, Liqiang Ni

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Popular methods for estimating the central subspace in regression require slicing a continuous response. However, slicing can result in loss of information and in some cases that loss can be substantial. We use intraslice covariances to construct improved inference methods for the central subspace. These methods are optimal within a class of quadratic inference functions and permit chi-squared tests of conditional independence hypotheses involving the predictors. Our experience gained through simulation is that the new method is never worse than existing methods, and can be substantially better.

Original languageEnglish (US)
Pages (from-to)65-74
Number of pages10
JournalBiometrika
Volume93
Issue number1
DOIs
StatePublished - Mar 2006

Keywords

  • Inverse regression estimation
  • Sliced inverse regression
  • Sufficient dimension reduction

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