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Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.
- Sales Rank: #1453976 in Books
- Published on: 2004-06-09
- Original language: English
- Number of items: 1
- Dimensions: 9.29" h x .72" w x 6.22" l, 1.00 pounds
- Binding: Paperback
- 320 pages
From the Publisher
Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.
From the Back Cover
The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists.
About the Author
Donald B. Rubin , PhD, is a John L. Loeb Professor of Statistics at Harvard University in Cambridge, MA.� He was named 2000-2001 Statistician of the Year by the Chicago Chapter of ASA.� His research interests include causal inference in experiments and observational studies, developing and applying statistical models to data in a variety of scientific disciplines, and the application of Bayesian and empirical Bayesian techniques.
Most helpful customer reviews
25 of 25 people found the following review helpful.
great introduction to multiple imputation and first by the inventor of the technique
By Michael R. Chernick
This book is a companion to the text by Rubin and Little. It specializes in one particular technique (multiple imputation)that has been highly successful particularly in nonrespnse surveys. Rubin has had a lot of good practical experience in missing data from his years of doing survey research at the Education Testing Service (ETS). He share this experience, knowledge and original ideas in this excellent text!
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