Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

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Donald B. Rubin, Andrew Gelman, Xiao-Li Meng
John Wiley & Sons, Sep 3, 2004 - Mathematics - 407 pages
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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.
 

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Contents

An overview of methods for causal inference from observational
3
Matching in observational studies by Paul R Rosenbaum
15
Estimating causal effects in nonexperimental studies
25
Medication cost sharing and drug spending in Medicare
37
A comparison of experimental and observational data analyses
49
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
Multimodality in mixture models and factor models by Eric Loken
203
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

Principal stratification by Constantine E Frangakis
97
Bridging across changes in classification systems by Nathaniel
117
Representing the Census undercount by multiple imputation
129
Statistical disclosure techniques based on multiple imputation
141
examples from the National
153
Propensity score estimation with missing data
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
Improved predictions of lynx trappings using a biological model
297
Record linkage using finite mixture models by Michael D Larsen
309
Identifying likely duplicates by record linkage in a survey
319
Applying structural equation models with incomplete data
331
Perceptual scaling by Ying Nian Wu ChengEn Guo
343
References
361
Index
401
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