Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation

by Ming T. Tan, Guo-Liang Tian, Kai Wang Ng

Overview

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-wor

Product Details

ISBN-13:	9781040173275
Publisher:	CRC Press
Publication date:	08/26/2009
Sold by:	Barnes & Noble
Format:	eBook
Pages:	344
File size:	2 MB

About the Author

Ming T. Tan is Professor of Biostatistics in the Department of Epidemiology and Preventive Medicine at the University of Maryland School of Medicine and Director of the Division of Biostatistics at the University of Maryland Greenebaum Cancer Center.

Guo-Liang Tian is Associate Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong.

Kai Wang Ng is Professor and Head of the Department of Statistics and Actuarial Science at the University of Hong Kong.

Table of Contents

Introduction. Optimization, Monte Carlo Simulation and Numerical Integration. Exact Solutions. Discrete Missing Data Problems. Computing Posteriors in the EM-Type Structures. Constrained Parameter Problems. Checking Compatibility and Uniqueness. Appendix. References. Indices.

What People are Saying About This

From the Publisher

In Bayesian Missing Data Problems, the authors provide a new and appealing approach to handle missing data problems (MDPs), based on noniterative methods. … the examples and real applications following key theorems and concepts are useful for readers to further understand the results and pinpoint major advantages or drawbacks about the proposed methodology. … I recommend this book as a valuable reference for researchers interested in MDPs, and I believe that the methodology described in the book should be included in the up-to-date literature on missing data. … the book stimulated my interest, suggesting an alternative way to think about MDPs. …
—Biometrics, June 2011

… [this book] sits nicely alongside Tanner’s Tools for Statistical Inference. … For those interested in Bayesian computational methods, this book will be of great interest. …
—International Statistical Review (2010), 78, 3

Editorial Reviews

In Bayesian Missing Data Problems, the authors provide a new and appealing approach to handle missing data problems (MDPs), based on noniterative methods. … the examples and real applications following key theorems and concepts are useful for readers to further understand the results and pinpoint major advantages or drawbacks about the proposed methodology. … I recommend this book as a valuable reference for researchers interested in MDPs, and I believe that the methodology described in the book should be included in the up-to-date literature on missing data. … the book stimulated my interest, suggesting an alternative way to think about MDPs. …
—Biometrics, June 2011

… [this book] sits nicely alongside Tanner’s Tools for Statistical Inference. … For those interested in Bayesian computational methods, this book will be of great interest. …
—International Statistical Review (2010), 78, 3

From the Publisher