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-wor
Autorentext
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.
Inhalt
Introduction. Optimization, Monte Carlo Simulation and Numerical Integration. Exact Solutions. Discrete Missing Data Problems. Computing Posteriors in the EM-Type Structures. Constrained Parameter Problems. Checking Compatibility and Uniqueness. Appendix. References. Indices.