Direction estimation in single-index regressions

Xiangrong Yin; R. Dennis Cook

doi:10.1093/biomet/92.2.371

Direction estimation in single-index regressions

Xiangrong Yin, R. Dennis Cook

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-review

54 Scopus citations

Abstract

We propose a general dimension-reduction method that combines the ideas of likelihood, correlation, inverse regression and information theory. We do not require that the dependence be confined to particular conditional moments, nor do we place restrictions on the predictors or on the regression that are necessary for methods like ordinary least squares and sliced-inverse regression. Although we focus on single-index regressions, the underlying idea is applicable more generally. Illustrative examples are presented.

Original language	English (US)
Pages (from-to)	371-384
Number of pages	14
Journal	Biometrika
Volume	92
Issue number	2
DOIs	https://doi.org/10.1093/biomet/92.2.371
State	Published - Jun 2005

Bibliographical note

Funding Information:
The authors would like to thank the editor and two referees whose suggestions led to a greatly improved paper. The work of Cook was supported in part by grants from the U.S. National Science Foundation.

Keywords

Central mean subspace
Central subspace
Dimension-reduction subspace
Regression graphics
Single-index model

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KW - Regression graphics

KW - Single-index model

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Direction estimation in single-index regressions

Abstract

Bibliographical note

Keywords

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