Applied Bayesian Modeling and Causal Inference from IncompleteData PerspectivesThis book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and realworld examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include:

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Contents
II Missing data modeling  109 
III Statistical modeling and computation  187 
IV Applied Bayesian inference  277 
361  
401  
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2004 John Wiley adjustment algorithm Applied Bayesian Modeling approach approximation assumptions Bayesian inference bias block causal effect Causal Inference census chapter codes computation conditional distribution control groups correlation cost sharing covariance matrix data set Estep ECME Edition emission line equation estimand estimated propensity score example factor function Gelman and XL Gibbs sampler given households Imbens IncompleteData Perspectives indicator Inference from IncompleteData intervals iteration latent variable model latent variables linear logistic regression Ltd ISBN lynx matching matrix maximum likelihood estimates MCMC measured Meng methods missing data mixture models Modeling and Causal multiple imputation multivariate observational studies pairs parameters population posterior distribution postterm potential outcomes predictive pretreatment prior distribution probability problem procedure proficiency random effects record linkage regression model resampling robit Rosenbaum and Rubin sample Section simulation SIR algorithm sparse coding statistical Table treated treatment effect treatment group values variance vector weighted