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Bayes and Empirical Bayes Methods for Data Analysis, Second Edition

By Bradley P. Carlin, Thomas A. Louis

Series Editor: Bradley P. Carlin, Chris Chatfield, Jim Zidek

Chapman and Hall/CRC – 2000 – 440 pages

Series: Chapman & Hall/CRC Texts in Statistical Science

Purchasing Options:

  • Hardback:
    978-1-58488-170-4
    June 21st 2000
    Out-of-print

Description

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.

Reviews

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

Contents

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

Name: Bayes and Empirical Bayes Methods for Data Analysis, Second Edition (eBook)Chapman and Hall/CRC 
Description: By Bradley P. Carlin, Thomas A. LouisSeries Editor: Bradley P. Carlin, Chris Chatfield, Jim Zidek. 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...
Categories: Statistics for the Biological Sciences, Mathematics & Statistics for Engineers, Quantitative Methods