Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Andrew Gelman,Xiao-Li Meng

Description

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.

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).

Series

Wiley Series in Probability and Statistics

To Purchase this product, please visit

https://dev.store.wiley.com/en-ca/Applied+Bayesian+Modeling+and+Causal+Inference+from+Incomplete-Data+Perspectives-p-9780470090442