Title

A Robust Inverse Regression Estimator

Keywords

Central subspace; Inverse regression estimator; Sufficient dimension reduction

Abstract

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. © 2006 Elsevier B.V. All rights reserved.

Publication Date

2-1-2007

Publication Title

Statistics and Probability Letters

Volume

77

Issue

3

Number of Pages

343-349

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.spl.2006.07.018

Socpus ID

33751538550 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33751538550

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