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Applied Bayesian modeling and causal inference from incomplete-data perspectives an essential journey with Donald Rubin's statistical family /

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 te...

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Bibliographic Details
Group Author: Rubin, Donald B; Gelman, Andrew; Meng, Xiao-Li
Published:
Literature type: eBook
Language: English
Series: Wiley series in probability and statistics
Subjects:
Bayesian statistical decision theory
Missing observations Statistics
MATHEMATICS > Probability & Statistics > Bayesian Analysis
Electronic books
Online Access: http://onlinelibrary.wiley.com/book/10.1002/0470090456
Summary: 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.
Carrier Form: 1 online resource (xix, 407 pages) : illustrations.
Bibliography: Includes bibliographical references (pages 361-400) and index.
ISBN: 0470090448
9780470090442
047009043X
9780470090435
0470090456
9780470090459
Index Number: QA279
CLC: O212.8
Contents: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; II Missing data modeling; III Statistical modeling and computation; IV Applied Bayesian inference; References; Index.

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