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Full Description
Empirical Bayes methods as envisioned by Herbert Robbins are becoming an essential element of the statistical toolkit. In Empirical Bayes: Tools, Rules, and Duals, Roger Koenker and Jiaying Gu offer a unified view of these methods. They stress recent computational developments for nonparametric estimation of mixture models, not only for the traditional Gaussian and Poisson settings, but for a wide range of other applications. Providing numerous illustrations where empirical Bayes methods are attractive, the authors give a detailed discussion of computational methods, enabling readers to apply the methods in new settings.
Contents
1. Introduction; 2. The empirical bayes paradigm; 3. Parametric empirical bayes methods; 4. Nonparametric empirical bayes methods; 5. Mixture models; 6. Heteroscedastic gaussian mixture models and longitudinal data; 7. Ranking and selection as compound decisions; 8. Empirical bayes methods for survival analysis; 9. Mixture models for categorical data; 10. Random coefficient models; 11. Inference for empirical bayes procedures; 12. Computational considerations; 13. Conclusion; Bibliography; Subject Index; Names Index.



