Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation: 32 (Chapman & Hall/CRC Biostatistics, 32) - Tapa blanda

Libro 19 de 151: Chapman & Hall/CRC Biostatistics

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

 
9780367385309: Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation: 32 (Chapman & Hall/CRC Biostatistics, 32)

Sinopsis

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.

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Acerca del autor

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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Otras ediciones populares con el mismo título

9781420077490: Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation: 32 (Chapman & Hall/CRC Biostatistics Series)

Edición Destacada

ISBN 10:  142007749X ISBN 13:  9781420077490
Editorial: Chapman and Hall/CRC, 2009
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