Partial central subspace and sliced average variance estimation

Yongwu Shao, R. Dennis Cook, Sanford Weisberg

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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
Issue number3
StatePublished - Mar 1 2009


  • Marginal tests
  • Partial IRE


Dive into the research topics of 'Partial central subspace and sliced average variance estimation'. Together they form a unique fingerprint.

Cite this