Shrinkage estimation in general linear models

We propose a lames-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. (C) 2008 Elsevier B.V. All rights reserved.