| Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation Contributor(s): Tan, Ming T. (Author), Tian, Guo-Liang (Author), Ng, Kai Wang (Author) |
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ISBN: 142007749X ISBN-13: 9781420077490 Publisher: CRC Press OUR PRICE: $133.00 Product Type: Hardcover - Other Formats Published: October 2009 Annotation: This book provides a systematic view of Bayesian methods to be used with missing data problems. The text presents a non-iterative approach that provides either explicit or non-iterative sampling calculation of posteriors. This computation includes exact numerical solutions, a conditional sampling approach via data augmentation, and a non-iterative sampling approach via EM-type algorithms. It illustrates the application of Bayesian analysis to important biostatistics problems and to other real-world applications, including the constrained parameter problem reformulated as a missing data problem.. The text includes S-PLUS/R computer codes to supplement existing functions for statistical distributions and stochastic processes |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Bayesian Analysis - Medical | Biostatistics - Medical | Pharmacology |
| Dewey: 519.542 |
| LCCN: 2009028155 |
| Series: Chapman & Hall/CRC Biostatistics |
| Physical Information: 0.9" H x 6.2" W x 9.3" (1.40 lbs) 346 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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-world problems, the authors focus on exact numerical solutions, a conditional sampling approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. After introducing the missing data problems, Bayesian approach, and posterior computation, the book succinctly describes EM-type algorithms, Monte Carlo simulation, numerical techniques, and optimization methods. It then gives exact posterior solutions for problems, such as nonresponses in surveys and cross-over trials with missing values. It also provides noniterative posterior sampling solutions for problems, such as contingency tables with supplemental margins, aggregated responses in surveys, zero-inflated Poisson, capture-recapture models, mixed effects models, right-censored regression model, and constrained parameter models. The text concludes with a discussion on compatibility, a fundamental issue in Bayesian inference. This book offers a unified treatment of an array of statistical problems that involve missing data and constrained parameters. It shows how Bayesian procedures can be useful in solving these problems. |