Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family
Copyright © 2004 John Wiley & Sons, Ltd
Editor(s): Andrew Gelman, Xiao-Li Meng
Published Online: 14 JUL 2005
Print ISBN: 9780470090435
Online ISBN: 9780470090459
Book Series: Wiley Series in Probability and Statistics
Series Editor(s): Walter A. Shewhart, Samuel S. Wilks
About this Book
About The Product
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.