A robust inverse regression estimator

Liqiang Ni, R. Dennis Cook

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410-428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. In this article, we propose a robust version of the IRE that only uses second moments of the predictor for estimation and inference, leading to better small sample results.

Original languageEnglish (US)
Pages (from-to)343-349
Number of pages7
JournalStatistics and Probability Letters
Issue number3
StatePublished - Feb 1 2007

Bibliographical note

Funding Information:
This research was supported in part by National Science Foundation Grant DMS-0405360.


  • Central subspace
  • Inverse regression estimator
  • Sufficient dimension reduction


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