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9780367385309

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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

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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.

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