Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Gelman, A. and Meng, X. (eds.)

ISBN 10: 047009043X / ISBN 13: 9780470090435

Published by John Wiley and Sons, 2004

About this Item

This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Book contains pencil markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:9780470090435.

Seller Inventory # 8624918

Bibliographic Details

• Title: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives
• Publisher: John Wiley and Sons
• Publication Date: 2004
• Language: English
• ISBN 10: 047009043X
• ISBN 13: 9780470090435
• Binding: Hardcover
• Condition: Good
• Weight: 900 grams
• Seller catalogs:
  • Mathematics

About this title

Synopsis

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.

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).