By Andrew Gelman, Xiao-Li Meng

ISBN-10: 0471486957

ISBN-13: 9780471486954

Statistical suggestions that take account of lacking information in a medical trial, census, or different experiments, observational experiences, and surveys are of accelerating significance. using more and more robust desktops and algorithms has made it attainable to review statistical difficulties from a Bayesian viewpoint. those themes are hugely lively examine components and feature very important purposes throughout quite a lot of disciplines.
This publication is a suite of articles from best researchers on statistical tools with regards to lacking info research, causal inference, and statistical modeling, together with a number of imputation, propensity rankings, instrumental variables, and Bayesian inference. The e-book is devoted to Professor Donald Rubin, at the get together of his sixtieth birthday, in popularity of his many and wide-ranging contributions to statistical data, relatively to the subject of statistical research with lacking data.

Provides an authoritative evaluation of a number of very important statistical issues for either study and applications.
Adopts a practical method of describing a variety of intermediate and complicated statistical techniques.
Covers key themes comparable to a number of imputation, propensity ratings, instrumental variables and Bayesian inference.
Includes a number functions from the social, future health, organic, and actual sciences.
Features evaluate chapters for every a part of the book.
Edited and authored via hugely revered researchers within the area.
Applied Bayesian Modeling and Causal Inference from Incomplete-Data views provides an summary with examples of those key issues appropriate for researchers in all parts of information. It adopts a pragmatic strategy appropriate for utilized statisticians operating in social and political sciences, organic and clinical sciences, and actual sciences, in addition to graduate scholars of facts and biostatistics.

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Extra info for Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics)

Example text

There are two key issues: covariate balance and the structure of matched sets. The simulation study by Gu and Rosenbaum (1993) considered a wide variety of issues and proposals, and concluded that balance and structure had both the largest and most consistent effect on the quality of matched samples. 18 MATCHING IN OBSERVATIONAL STUDIES—ROSENBAUM Covariate balance using propensity scores When x is of moderate to high dimension k—that is, when there are many covariates— it will be difficult if not impossible to match most treated subjects to controls with the same value of x.

1 Distribution of individual characteristics and drug expenditures by drug coverage status using data from the 1995 Medicare Current Beneficiary Survey. The percentages represent the proportion of the population matching that specific characteristic.

Additional information on sample selection for the MCBS can be found in the article by Adler (1994). Study sample For the purposes of this study, I included those beneficiaries most likely to voluntarily enroll in the new Medicare prescription drug plans. , employer sponsored, self-purchased), with or without drug coverage. Excluded were beneficiaries with VA, state drug coverage plans, local drug coverage plans, which are typically more generous than the new benefit. Dual enrollees, those with both Medicaid and Medicare, will be moved from Medicaid drug coverage to Medicare drug coverage.

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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) by Andrew Gelman, Xiao-Li Meng


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