Sufficient dimension reduction in regressions across heterogeneous subpopulations

    Authors

    L. Q. Ni;R. D. Cook

    Comments

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    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

    WOS:000234129000005

    ISSN

    1369-7412

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