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Edition: 1
Publisher: Wiley
Binding: Kindle Edition
ISBN: B007EKOAVE
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics)
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. Medical books Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives . 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 Medical books Applied Bayesian Modeling And Causal Inference From Incomplete-data Perspectives. payment | shipping rates | returns Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) ISBN: 047009043X Title: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) Author: Book Condition: New Item Notes: Binding: Hardcover Publication Date: 2004-09-03 Publisher: Wiley Pages: 440 Height: 1.1800 inches Width: 6.2200 inches Weight: 1.8100 pounds About U
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payment | shipping rates | returns Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) ISBN: 047009043X Title: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) Author: Book Condition: New Item Notes: Binding: Hardcover Publication Date: 2004-09-03 Publisher: Wiley Pages: 440 Height: 1.1800 inches Width: 6.2200 inches Weight: 1.8100 pounds About U
format hardback language english publication year 23 07 2004 series wiley series in probability and statistics subject mathematics sciences subject 2 mathematics title applied bayesian modeling and causal inference from incomplete data perspectives an essential journey with donald rubin s statistical family author gelman andrew editor meng xiao li editor rubin donald b editor publisher john wiley sons inc publication date sep 17 2004 pages 436 binding hardcover edition 1 st dimensions 6 25 wx 9
Statistical techniques that take account of missing data in a clinical trial, census, or other experiments, observational studies, and surveys are of increasing importance. The use of increasingly powerful computers and algorithms has made it possible to study statistical problems from a Bayesian perspective. These topics are highly active research areas and have important applications across a wide range of disciplines. This book is a collection of articles from leading researchers on statistical methods relating to missing data analysis, causal inference, and statistical modeling, including
"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 rangMedical Book Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives
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.
