Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

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.

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives features in the following genres: Mathematics

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives is available in Hardback

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives was written by Andrew Gelman, XiaoLi Meng and published by John Wiley & Sons, Inc. an imprint of Wiley

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives has 407 pages

Yes it is part of Wiley Series in Probability and Statistics series