Bayesian Missing Data Problems: Em, Data Augmentation And Noniterative Computation (chapman & Hall/crc Biostatistics Series)
by Kai Wang Ng /
2009 / English / PDF
2.8 MB Download
Bayesian Missing Data Problems: EM, Data Augmentation and
Noniterative Computation
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.
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.
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.
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.