Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives 1

Vetenskap & teknik

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Andrew Gelman • Xiao-Li Meng

Inbunden

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440 sidor
2004

This bookbrings together acollection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics andreal-worldexampleswhich 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.

Författare: Andrew Gelman, Xiao-Li Meng
Format: Inbunden
ISBN: 9780470090435
Språk: Engelska
Antal sidor: 440
Utgivningsdatum: 2004-07-23
Förlag: John Wiley & Sons Inc