Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives / Wiley Series in Probability and Statistics (PDF)
(Sprache: Englisch)
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...
statistical methods relating to missing data analysis, including
multiple imputation, propensity scores, instrumental variables, and
Bayesian inference. Covering new research topics
and real-world...
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Produktdetails
Produktinformationen zu „Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives / Wiley Series in Probability and Statistics (PDF)“
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.
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.
Autoren-Porträt
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).
Bibliographische Angaben
- 2004, 1. Auflage, 436 Seiten, Englisch
- Herausgegeben: Andrew Gelman, Xiao-Li Meng
- Verlag: John Wiley & Sons
- ISBN-10: 0470090448
- ISBN-13: 9780470090442
- Erscheinungsdatum: 31.08.2004
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- Grösse: 2.60 MB
- Mit Kopierschutz
Sprache:
Englisch
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