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Risk‐reducing shrinkage estimation for generalized linear models

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  • Dan J. Spitzner

Abstract

Summary. Empirical Bayes techniques for normal theory shrinkage estimation are extended to generalized linear models in a manner retaining the original spirit of shrinkage estimation, which is to reduce risk. The investigation identifies two classes of simple, all‐purpose prior distributions, which supplement such non‐informative priors as Jeffreys's prior with mechanisms for risk reduction. One new class of priors is motivated as optimizers of a core component of asymptotic risk. The methodology is evaluated in a numerical exploration and application to an existing data set.

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  • Dan J. Spitzner, 2005. "Risk‐reducing shrinkage estimation for generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 183-196, February.
    • Handle: RePEc:bla:jorssb:v:67:y:2005:i:1:p:183-196
    DOI: 10.1111/j.1467-9868.2005.00495.x
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    Cited by:

    1. An, Lihua & Nkurunziza, Sévérien & Fung, Karen Y. & Krewski, Daniel & Luginaah, Isaac, 2009. "Shrinkage estimation in general linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2537-2549, May.

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