Using intraslice covariances for improved estimation of the central subspace in regression

    Authors

    R. D. Cook;L. Q. Ni

    Comments

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    Abbreviated Journal Title

    Biometrika

    Keywords

    inverse regression estimation; sliced inverse regression; sufficient; dimension reduction; SUFFICIENT DIMENSION REDUCTION; SLICED INVERSE REGRESSION; Biology; Mathematical & Computational Biology; Statistics & Probability

    Abstract

    Popular methods for estimating the central subspace in regression require slicing a continuous response. However, slicing can result in loss of information and in some cases that loss can be substantial. We use intraslice covariances to construct improved inference methods for the central subspace. These methods are optimal within a class of quadratic inference functions and permit chi-squared tests of conditional independence hypotheses involving the predictors. Our experience gained through simulation is that the new method is never worse than existing methods, and can be substantially better.

    Journal Title

    Biometrika

    Volume

    93

    Issue/Number

    1

    Publication Date

    1-1-2006

    Document Type

    Article

    Language

    English

    First Page

    65

    Last Page

    74

    WOS Identifier

    WOS:000235921600005

    ISSN

    0006-3444

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