Originally published in 1970; with a second edition in 1989. Empirical Bayes methods use some of the apparatus of the pure Bayes approach, but an actual prior distribution is assumed to generate the data sequence. It can be estimated thus producing empirical Bayes estimates or decision rules.
In this second edition, details are provided of the derivation and the performance of empirical Bayes rules for a variety of special models. Attention is given to the problem of assessing the goodness of an empirical Bayes estimator for a given set of prior data. Chapters also focus on alternatives to the empirical Bayes approach and actual applications of empirical Bayes methods.
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Table of Contents
Preface. Notation and abbreviations. 1. Introduction to Bayes and Empirical Bayes Methods 2. Estimation of the Prior Distribution 3. Empirical Bayes Point Estimation 4. Empirical Bayes Point Estimation: Vector Parameters 5. Testing of Hypotheses 6. Bayes and Empirical Bayes Interval Estimation 7. Alternatives to Empirical Bayes 8. Applications of EB methods