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

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

Softcover

ISBN 10: 0367385309 ISBN 13: 9780367385309

Verlag: Chapman and Hall/CRC, 2019

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den 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.

Details zur Ausgabe

• Verlag: Chapman and Hall/CRC
• Erscheinungsdatum: 2019
• Sprache: Englisch
• ISBN 10: 0367385309
• ISBN 13: 9780367385309
• Einband: Taschenbuch
• Anzahl der Seiten: 348
• Kontakt zum Hersteller: preigu GmbH & Co. KG; mail@preigu.de; ; Lengericher Landstr. 19; Osnabrück; Niedersachsen; 49078; Deutschland