Title
Sufficient dimension reduction in regressions across heterogeneous subpopulations
Abbreviated Journal Title
J. R. Stat. Soc. Ser. B-Stat. Methodol.
Keywords
general partial sliced inverse regression; partial sliced inverse; regression; sliced inverse regression; sufficient dimension reduction; SLICED INVERSE REGRESSION; SQUARES; MODELS; Statistics & Probability
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.
Journal Title
Journal of the Royal Statistical Society Series B-Statistical Methodology
Volume
68
Publication Date
1-1-2006
Document Type
Article
Language
English
First Page
89
Last Page
107
WOS Identifier
ISSN
1369-7412
Recommended Citation
"Sufficient dimension reduction in regressions across heterogeneous subpopulations" (2006). Faculty Bibliography 2000s. 6471.
https://stars.library.ucf.edu/facultybib2000/6471
Comments
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