Shrinkage estimation in general linear models
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.
Year of publication: |
2009
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Authors: | An, Lihua ; Nkurunziza, Sévérien ; Fung, Karen Y. ; Krewski, Daniel ; Luginaah, Isaac |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 53.2009, 7, p. 2537-2549
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Publisher: |
Elsevier |
Saved in:
Online Resource
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