Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Professor Gelman has edited a book containing 29 articles dealing primarily with real applications of Bayesian methods for causal inference and the treatment of incomplete data.
It contains a collection of the best work in applied statistics by prominent statisticians. In addition to learning the wide variety of problems that have been solved using the Bayesian approach (particularly in the medical field) the reader can learn and appreciate the power and ease of interpretation of Bayesian results.

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This 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 real-world 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:
Comprehensive coverage of an imporant area for both research and applications.
Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.
Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.
Includes a number of applications from the social and health sciences.
Edited and authored by highly respected researchers in the area.

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