Marginal tests with sliced average variance estimation

Shao Yongwu, R. Dennis Cook, Sanford Weisberg

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

We present a new computationally feasible test for the dimension of the central subspace in a regression problem based on sliced average variance estimation. We also provide a marginal coordinate test. Under the null hypothesis, both the test of dimension and the marginal coordinate test involve test statistics that asymptotically have chi-squared distributions given normally distributed predictors, and have a distribution that is a linear combination of chi-squared distributions in general.

Original languageEnglish (US)
Pages (from-to)285-296
Number of pages12
JournalBiometrika
Volume94
Issue number2
DOIs
StatePublished - 2007

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT We thank the referees and the editor for their useful comments. R. D. Cook and Y. Shao were supported by the U.S. National Science Foundation.

Keywords

  • Marginal coordinate test
  • Sufficient dimension reduction

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