Partial central subspace and sliced average variance estimation

Yongwu Shao, R. Dennis Cook, Sanford Weisberg

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Sliced average variance estimation is one of many methods for estimating the central subspace. It was shown to be more comprehensive than sliced inverse regression in the sense that it consistently estimates the central subspace under mild conditions while slice inverse regression may estimate only a proper subset of the central subspace. In this paper we extend this method to regressions with qualitative predictors. We also provide tests of dimension and a marginal coordinate hypothesis test. We apply the method to a data set concerning lakes infested by Eurasian Watermilfoil, and compare this new method to the partial inverse regression estimator.

Original languageEnglish (US)
Pages (from-to)952-961
Number of pages10
JournalJournal of Statistical Planning and Inference
Volume139
Issue number3
DOIs
StatePublished - Mar 1 2009

Keywords

  • Marginal tests
  • Partial IRE

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