Bayes and Empirical Bayes Methods for Data Analysis

Descripción:

Verschmutzung / Wasserschaden; Abnutzung / Risse - leicht; Gebrochener Buchrucken / Seiten oder Softcover umgeknickt; Vergilbt / ausgeblichen. Recent advances in computing have made it possible to evaluate complex models, leading to the increased popularity of Bayes and empirical Bayes (EB) methods in statistics. This work serves as a practical reference for both practicing statisticians and graduate students, introducing Bayes and EB methods while demonstrating their effectiveness in applied settings. It emphasizes implementation using modern Markov chain Monte Carlo (MCMC) techniques, showcasing how well-structured Bayes and EB procedures perform in both frequentist and Bayesian contexts without delving into philosophical debates. The authors adopt a practical approach, providing real solution methods for researchers facing challenging problems. They begin by outlining the decision-theoretic tools necessary for comparing procedures, followed by an introduction to the fundamentals of Bayes and EB approaches. The performance of these methods is evaluated across various scenarios, highlighting their strengths and weaknesses. The latter half of the work focuses on applications, offering an in-depth discussion of contemporary Bayesian computation methods, including the Gibbs sampler and the Metropolis-Hastings algorithm. It also covers data analytic tasks and provides guidelines for utilizing various specialized methods and models. The book concludes with three comprehensive case studies based on real datasets, illustrating the application of the discussed methods. N° de ref. del artículo 367e6af7-2ead-4252-b9a4-efcc3d19c18f

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Sinopsis:

Reseña del editor: 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.