Abstract
Empirical Bayes estimators for the parameters in the general linear regression model are presented. These estimators by-pass exact knowledge of the prior distribution of the parameters by means of supplementary information from similar independent experiments. Indexing quantities are obtained which summarize the information contained in a typical experimental situation, and Monte Carlo simulation is employed to determine the degree of mean square improvement of empirical Bayes estimators over maximum likelihood. Particular emphasis is given to the simple non-orthogonal linear regression model.
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