Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

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John Wiley & Sons, Sep 3, 2004 - Mathematics - 407 pages
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.

 

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Contents

Matching in observational studies by Paul R Rosenbaum
15
14
30
Medication cost sharing and drug spending in Medicare
37
Fixing broken experiments using the propensity score
61
The propensity score with continuous treatments
73
Causal inference with instrumental variables by Junni L Zhang
85
Principal stratification by Constantine E Frangakis
97
Schenker
117
Modeling the covariance and correlation matrix of repeated measures
215
a simple robust alternative to logistic and probit
227
Using EM and data augmentation for the competing risks model
239
Mixed effects models and the EM algorithm
253
The samplingimportance resampling algorithm by KimHung Li
265
Whither applied Bayesian inference? by Bradley P Carlin
279
Efficient EMtype algorithms for fitting spectral lines in highenergy
285
by Cavan Reilly and Angelique Zeringue
297

by Roderick J A Little Fang Liu and Trivellore E Raghunathan
141
by Ralph B DAgostino Jr
163
Sensitivity to nonignorability in frequentist inference
175
Statistical modeling and computation by D Michael Titterington
189
Treatment effects in beforeafter data by Andrew Gelman
195
Multimodality in mixture models and factor models by Eric Loken
203
of prostitutes by Thomas R Belin Hemant Ishwaran Naihua Duan
319
by Hal S Stern and Yoonsook Jeon
331
and Song Chun Zhu
343
References
361
Index
401
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About the author (2004)

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).

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