Evaluation of empirical Bayes estimators for small numbers of past samples
SUMMARY: The usual technique for evaluating the performance of empirical Bayes estimators for small numbers, N, of past observations is to compare by Monte Carlo techniques the global risk for the empirical Bayes estimator to the risk obtained for some optimal non-Bayes estimator δ. Here it is shown, under fairly general conditions, that if the prior variance ß2 is small relative to the Bayes risk of δ, and if N≠ 0, then one can always find an estimator that is better than δ, regardless of the form of the prior G or the magnitude of N. © 1971 Oxford University Press.
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