Synopsis
Recent advances in computing-leading to the ability to evaluate increasingly complex models-has resulted in a growing popularity of Bayes and empirical Bayes (EB) methods in statistical practice. Bayes and Empirical Bayes Methods for Data Analysis answers the need for a ready reference that can be read and appreciated by practicing statisticians as well as graduate students. It introduces Bayes and EB methods, demonstrates their usefulness in challenging applied settings, and shows how they can be implemented using modern Markov chain Monte Carlo (MCMC) computational methods. Avoiding philosophical nit-picking, it shows how properly structured Bayes and EB procedures have good frequentist and Bayesian performance both in theory and practice.
The authors have chosen a very practical focus for their work, offering real solution methods to researchers with challenging problems. Beginning with an outline of the decision-theoretic tools needed to compare procedures, the book presents the basics of Bayes and EB approaches. The authors evaluate the frequentist and empirical Bayes performance of these approaches in a variety of settings and identify both virtues and drawbacks. The second half of the book stresses applications. If offers an extensive discussion of modern Bayesian computation methods-including the Gibbs sampler and the Metropolis-Hastings algorithm. It describes data analytic tasks, and offers guidelines on using a variety of special methods and models. The authors conclude with three fully worked case studies of real data sets.
Review
About the Second Edition:
The writing is excellent and the worked examples are also excellent for understanding the methods. In summary, I recommend Bayes and Empirical Bayes Methods for Data Analysis for advanced graduate students and all research workers.
-Olaf Berke in Computational Statistics & Data Analysis, January 2001
...particularly commends the book to practising biometricians who want to explore the potential for using Bayesian methods in their own work.
-Biometrics, Vol. 57, No. 3, September 2001
...the book is beautifully written and many of the questions it raises - and most of the answers provided - are of concern for the applied statistician whether Bayesian, frequentist or likelihoodist.
-Guadalupe Gomez, Statistics in Medicine Vol 21, #23 Dec 15 2002.
About the First Edition:
...an important and timely addition to applied statistics...the writing is excellent, and the authors are able to present an amazing amount of material cogently in [a] smaller book...the reader reaps the benefits of being in the hands of a true master...
-Journal of American Statistical Association
...an excellent exposition of Bayes and empirical Bayes methods...gives a well-balanced mathematical and computational treatment of Bayes and empirical Bayes paradigms, and nicely examines the similarities and contrasts in the two approaches.
-Short Book Reviews of the ISI
...and impressive compendium of the mathematical techniques underlying Bayes and empirical Bayes methods...
-American Journal of Epidemiology
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