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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics)
Andrew Gelman, Xiao-Li MengHow much do you like this book?
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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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Year:
2004
Edition:
1
Publisher:
John Wiley & Sons
Language:
english
Pages:
436
ISBN 10:
047009043X
ISBN 13:
9780470090435
Series:
WILEY SERIES IN PROBABILITY AND STATISTICS
File:
PDF, 5.11 MB
Your tags:
IPFS:
CID , CID Blake2b
english, 2004
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