Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives
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Beschreibung
This book brings together a collection of articles onstatistical methods relating to missing data analysis, includingmultiple imputation, propensity scores, instrumental variables, andBayesian inference. Covering new research topicsand real-world examples which do not feature in manystandard texts. The book is dedicated to Professor Don Rubin(Harvard). Don Rubin has made fundamental contributions tothe study of missing data.
Key features of the book include:
* Comprehensive coverage of an imporant area for both researchand applications.
* Adopts a pragmatic approach to describing a wide range ofintermediate and advanced statistical techniques.
* Covers key topics such as multiple imputation, propensityscores, instrumental variables and Bayesian inference.
* Includes a number of applications from the social and healthsciences.
* Edited and authored by highly respected researchers in thearea.
Autor*in
Andrew Gelman
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"I congratulate the editors on this volume; it really is anessential and very enjoyable journey with Don Rubin's statisticalfamily." (Biometrics, September 2006)
"...contains much current important work..."(Technometrics, November 2005)
"This a useful reference book on an important topic withapplications to a wide range of disciplines." (CHOICE,September 2005)
"With this variety of papers, the reader is bound to findsome papers interesting..." (Journal of AppliedStatistics, Vol.32, No.3, April 2005)
"I strongly recommend that libraries have a copy of thisbook in their reference section." (Journal of the RoyalStatistical Society Series A, June 2005)
"...a very useful addition to academic libraries..."(Short Book Reviews, Vol.24, No.3, December 2004)
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