Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives - Hardcover

Hardcover

ISBN 10: 047009043X ISBN 13: 9780470090435

Publisher: Wiley, 2004

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 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).

From the Back Cover

Statistical techniques that take account of missing data in a clinical trial, census, or other experiments, observational studies, and surveys are of increasing importance. The use of increasingly powerful computers and algorithms has made it possible to study statistical problems from a Bayesian perspective. These topics are highly active research areas and have important applications across a wide range of disciplines.

This book is a collection of articles from leading researchers on statistical methods relating to missing data analysis, causal inference, and statistical modeling, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. The book is dedicated to Professor Donald Rubin, on the occasion of his 60th birthday, in recognition of his many and wide-ranging contributions to statistics, particularly to the topic of statistical analysis with missing data.

• Provides an authoritative overview of several important statistical topics 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 range of applications from the social, health, biological, and physical sciences.
• Features overview chapters for each part of the book.
• Edited and authored by highly respected researchers in the area.

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives presents an overview with examples of these key topics suitable for researchers in all areas of statistics. It adopts a practical approach suitable for applied statisticians working in social and political sciences, biological and medical sciences, and physical sciences, as well as graduate students of statistics and biostatistics.

This book is a collection of articles from leading researchers on statistical methods relating to missing data analysis, causal inference, and statistical modeling, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. The book is dedicated to Professor Donald Rubin, on the occasion of his 60th birthday, in recognition of his many and wide-ranging contributions to statistics, particularly to the topic of statistical analysis with missing data.

• Provides an authoritative overview of several important statistical topics 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 range of applications from the social, health, biological, and physical sciences.
• Features overview chapters for each part of the book.
• Edited and authored by highly respected researchers in the area.

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives presents an overview with examples of these key topics suitable for researchers in all areas of statistics. It adopts a practical approach suitable for applied statisticians working in social and political sciences, biological and medical sciences, and physical sciences, as well as graduate students of statistics and biostatistics.

Edition details

• Publisher: Wiley
• Publication date: 2004
• Language: English
• ISBN 10: 047009043X
• ISBN 13: 9780470090435
• Binding: Hardcover
• Edition number: 1
• Number of pages: 440
• Editor: Gelman Andrew, Meng Xiao-Li