Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family

Editor(s): Andrew Gelman, Xiao-Li Meng

Published Online: 14 JUL 2005

Print ISBN: 9780470090435

Online ISBN: 9780470090459

DOI: 10.1002/0470090456

Series Editor(s): Walter A. Shewhart, Samuel S. Wilks

About this Book

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.

Table of contents

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  1. Part I: Casual Inference and Observational Studies

  2. Part II: Missing Data Modeling

  3. Part III: Statistical Modeling and Computation

  4. Part IV: Applied Bayesian Inference

    1. Chapter 29

      Identifying Likely Duplicates by Record Linkage in a Survey of Prostitutes (pages 319–329)

      Thomas R. Belin, Hemant Ishwaran, Naihua Duan, Sandra H. Berry and David E. Kanouse

    2. Chapter 31

      Perceptual Scaling (pages 343–360)

      Ying Nian Wu, Cheng-En Guo and Song Chun Zhu

    1. You have free access to this content
    1. You have free access to this content
    1. You have free access to this content

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