A note on shrinkage sliced inverse regression

    Authors

    L. Q. Ni; R. D. Cook;C. L. Tsai

    Comments

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    Abbreviated Journal Title

    Biometrika

    Keywords

    Garotte; Lasso; shrinkage estimator; sliced inverse regression; sufficient dimension reduction; SUFFICIENT DIMENSION REDUCTION; PRINCIPAL HESSIAN DIRECTIONS; BINARY; RESPONSE; LASSO; MODEL; SELECTION; Biology; Mathematical & Computational Biology; Statistics & Probability

    Abstract

    We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage sliced inverse regression estimator, which provides easier interpretations and better prediction accuracy without assuming a parametric model. The shrinkage sliced inverse regression approach can be employed for both single-index and multiple-index models. Simulation studies suggest that the new estimator performs well when its tuning parameter is selected by either the Bayesian information criterion or the residual information criterion.

    Journal Title

    Biometrika

    Volume

    92

    Issue/Number

    1

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    242

    Last Page

    247

    WOS Identifier

    WOS:000228099300019

    ISSN

    0006-3444

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