Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives : An Essential Journey with Donald Rubin's Statistical Family
Language: English
Published by John Wiley & Sons Sep 2004, 2004
Series: Book 213 of 358 - Wiley Series in Probability and Statistics
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 143.38
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Add to basketItem description from seller
Neuware - 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.
Seller Inventory # 9780470090435
- Title
- Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives : An Essential Journey with Donald Rubin's Statistical Family
- Author
- Meng
- Publisher
- John Wiley & Sons Sep 2004
- Publication year
- 2004
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 047009043X
- ISBN 13
- 9780470090435
- Item weight
- 824 grams
- Dimensions
- 240x161x28 mm
- Series
- Book 213 of 358: Wiley Series in Probability and Statistics
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 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.
"Synopsis" may belong to another edition of this title.
About the Author
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).
Xiao-Li Meng, Department of Statistics, Harvard University, USA.
"About the title" may belong to another edition of this title.
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