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Donald B. Rubin - Multiple Imputation for Nonresponse in Surveys - 9780471655749 - V9780471655749
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Multiple Imputation for Nonresponse in Surveys

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Description for Multiple Imputation for Nonresponse in Surveys Paperback. 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. Series: Wiley Classics Library. Num Pages: 320 pages, illustrations. BIC Classification: JH; PB. Category: (P) Professional & Vocational. Dimension: 236 x 156 x 19. Weight in Grams: 486.
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 ... Read more

Product Details

Format
Paperback
Publication date
2004
Publisher
John Wiley and Sons Ltd United States
Number of pages
320
Condition
New
Series
Wiley Classics Library
Number of Pages
320
Place of Publication
, United States
ISBN
9780471655749
SKU
V9780471655749
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50

About Donald B. Rubin
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 ... Read more

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