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

This book presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors, based on the inverse Bayes formulae. The authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. They describe Monte Carlo simulation, numerical techniques, and optimization methods. The book illustrates the methods with biostatistical models and real-world applications, including mixed effects and hierarchical models, nonresponse and contingency tables, and the constrained parameter problem reformulated as a missing data problem.

Author: Ming T. Tan,Guo-Liang Tian,Kai Wang Ng
Publisher: CRC Press
Published: 11/04/2019
Pages: 346
Binding Type: Paperback
Weight: 1.55lbs
Size: 9.20h x 6.10w x 0.80d
ISBN: 9780367385309

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.