Bayes and empirical Bayes methods for data analysis

著者

• Carlin, Bradley P.
• Louis, Thomas A.

書誌事項

Bayes and empirical Bayes methods for data analysis

Bradley P. Carlin and Thomas A. Louis

（Texts in statistical science）

Chapman & Hall/CRC, c2000

2nd ed

注記

Bibliography: p. [381]-406

Includes indexes

内容説明・目次

内容説明

In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners. With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice.

目次

APPROACHES FOR STATISTICAL INFERENCE Introduction Motivating Vignettes Defining the approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models THE BAYES APPROACH Introduction Prior Distributions Bayesian Inference Model Assessment THE EMPIRICAL BAYES APPROACH Introduction Nonparametric EB (NPEB) Point Estimation Parametric EB (PEB) Point Estimation Computation via the EM Algorithm Interval Estimation Generalization to Regression Structures PERFORMANCE OF BAYES PROCEDURES Bayesian Processing Frequentist Performance: Point Estimates Frequentist Performance: Confidence Intervals Empirical Bayes Performance Design of Experiments BAYESIAN COMPUTATION Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods MODEL CRITICISM AND SELECTION Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors via Sampling over the Model Space Other Model Selection Methods SPECIAL METHODS AND MODELS Estimating Histograms and Ranks Order Restricted Inference Nonlinear Models Longitudinal Data Models Continuous and Categorical Time Series Survival Analysis and Frailty Models Sequential Analysis Spatial and Spatio-Temporal Models CASE STUDIES Analysis of Longitudinal AIDS Data Robust Analysis of Clinical Trials Spatio-Temporal Mapping of Lung Cancer Rates APPENDICES A Distributional Catalog Decision Theory Software Guide

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詳細情報

• NII書誌ID(NCID): BA47790862
• ISBN:
  • 1584881704
• LCCN: 00038242
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Boca Raton
• ページ数/冊数: xvii, 419 p.
• 大きさ: 24 cm
• 分類:
  • DC21 : 519.5/42
• 件名:
  • LCSH : Bayesian statistical decision theory
• 親書誌ID:
  • BA22776704