Title

A note on shrinkage sliced inverse regression

Authors

Authors

L. Q. Ni; R. D. Cook;C. L. Tsai

Comments

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Abbreviated Journal Title

Biometrika

Keywords

Garotte; Lasso; shrinkage estimator; sliced inverse regression; sufficient dimension reduction; SUFFICIENT DIMENSION REDUCTION; PRINCIPAL HESSIAN DIRECTIONS; BINARY; RESPONSE; LASSO; MODEL; SELECTION; Biology; Mathematical & Computational Biology; Statistics & Probability

Abstract

We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage sliced inverse regression estimator, which provides easier interpretations and better prediction accuracy without assuming a parametric model. The shrinkage sliced inverse regression approach can be employed for both single-index and multiple-index models. Simulation studies suggest that the new estimator performs well when its tuning parameter is selected by either the Bayesian information criterion or the residual information criterion.

Journal Title

Biometrika

Volume

92

Issue/Number

1

Publication Date

1-1-2005

Document Type

Article

Language

English

First Page

242

Last Page

247

WOS Identifier

WOS:000228099300019

ISSN

0006-3444

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