Shrinkage estimation in general linear models

Lihua An, Sévérien Nkurunziza, Karen Y. Fung, Daniel Krewski and Isaac Luginaah

Computational Statistics & Data Analysis, 2009, vol. 53, issue 7, 2537-2549

Abstract: We propose a James-Stein-type shrinkage estimator for the parameter vector in a general linear model when it is suspected that some of the parameters may be restricted to a subspace. The James-Stein estimator is shown to demonstrate asymptotically superior risk performance relative to the conventional least squares estimator under quadratic loss. An extensive simulation study based on a multiple linear regression model and a logistic regression model further demonstrates the improved performance of this James-Stein estimator in finite samples. The application of this new estimator is illustrated using Ontario newborn infants data spanning four fiscal years.

Date: 2009
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:7:p:2537-2549

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