Optimal sufficient dimension reduction for the conditional mean in multivariate regression

Jae Keun Yoo, R. Dennis Cook

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

The aim of this article is to develop optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression. The context is roughly the same as that of a related method by Cook & Setodji (2003), but the new method has several advantages. It is asymptotically optimal in the sense described herein and its test statistic for dimension always has a chi-squared distribution asymptotically under the null hypothesis. Additionally, the optimal method allows tests of predictor effects. A comparison of the two methods is provided.

Original languageEnglish (US)
Pages (from-to)231-242
Number of pages12
JournalBiometrika
Volume94
Issue number1
DOIs
StatePublished - Mar 1 2007

Keywords

  • Multivariate conditional mean
  • Multivariate regression
  • Predictor effect test
  • Sufficient dimension reduction

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