Applied Bayesian Modeling and Causal Inference from Incomplete-Data PerspectivesStatistical 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.
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 |