Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Andrew Gelman - Collection Wiley Series in Probability and Statistics

410 pages, parution le 17/11/2004

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Résumé

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.

Sommaire

Casual inference and observational studies
- An overview of methods for causal inference from observational studies
- Estimating causal effects in nonexperimental studies
- Medication cost sharing and drug spending in Medicare
- Comparison of experimental and observational data analyses
- Fixing broken experiments using the propensity score
- The propensity score with continuous treatments
- Causal inference with instrumental variables
- Principal stratification
Missing data modeling.
- Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues
- Bridging across changes in classification systems
- Representing the Census undercount by multiple imputation of households
- Statistical disclosure techniques based on multiple imputation
- Designs producing balanced missing data: examples from the National Assessment of Educational Progress
- Propensity score estimation with missing data
- Sensitivity to nonignorability in frequentist inference
Statistical modeling and computation.
- Statistical modeling and computation
- Treatment effects in before-after data
- Multimodality in mixture models and factor models
- Modeling the covariance and correlation matrix of repeated measures
- Robit regression: a simple robust alternative to logistic and probit regression
- Using EM and data augmentation for the competing risks model,
- Mixed effects models and the EM algorithm,
- The sampling/importance resampling algorithm
Applied Bayesian inference.
- Whither applied Bayesian inference?
- Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics
- Improved predictions of lynx trappings using a biological model,
- Record linkage using finite mixture models
- Identifying likely duplicates by record linkage in a survey of prostitutes
- Applying structural equation models with incomplete data
- Perceptual scaling

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Andrew Gelman
Collection	Wiley Series in Probability and Statistics
Parution	17/11/2004
Nb. de pages	410
Format	15,5 x 23,5
Couverture	Relié
Poids	820g
Intérieur	Noir et Blanc
EAN13	9780470090435
ISBN13	978-0-470-09043-5