Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1154874 | Statistics & Probability Letters | 2007 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Liqiang Ni, R. Dennis Cook,