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Bayesian Missing Data Problems - EM, Data Augmentation and Noniterative Computation (Paperback)
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Bayesian Missing Data Problems - EM, Data Augmentation and Noniterative Computation (Paperback)
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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.
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