Read Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family by Andrew Gelman Xiao-Li Meng Online

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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 hasThis 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....

Title : Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family
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ISBN : 9780470090435
Format Type : Hardcover
Number of Pages : 407 Pages
Status : Available For Download
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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family Reviews