Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives Hardback
Edited by Andrew (Columbia University, NY) Gelman, Xiao-Li (Harvard University, MA) Meng
Part of the Wiley Series in Probability and Statistics series
Hardback
Description
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.
Information
-
Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Hardback
- Pages:440 pages
- Publisher:John Wiley & Sons Inc
- Publication Date:23/07/2004
- Category:
- ISBN:9780470090435
Information
-
Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Hardback
- Pages:440 pages
- Publisher:John Wiley & Sons Inc
- Publication Date:23/07/2004
- Category:
- ISBN:9780470090435
