Title

Sufficient Dimension Reduction In Regressions Across Heterogeneous Subpopulations

Keywords

General partial sliced inverse regression; Partial sliced inverse regression; Sliced inverse regression; Sufficient dimension reduction

Abstract

Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this method to regressions with qualitative predictors and developed a method, partial sliced inverse regression, under the assumption that the covariance matrices of the continuous predictors are constant across the levels of the qualitative predictor. We extend partial sliced inverse regression by removing the restrictive homogeneous covariance condition. This extension, which significantly expands the applicability of the previous methodology, is based on a new estimation method that makes use of a non-linear least squares objective function. © 2006 Royal Statistical Society.

Publication Date

2-1-2006

Publication Title

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volume

68

Issue

1

Number of Pages

89-107

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1111/j.1467-9868.2005.00534.x

Socpus ID

33645024090 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33645024090

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