Multiple Imputation for Nonresponse in Surveys
Multiple Imputation for Nonresponse in Surveys by Donald B. Rubin, published by John Wiley & Sons on June 9, 2004, is a comprehensive exploration of handling nonresponse in sample surveys and censuses. This subsequent edition spans 320 pages and is presented in English. The book demonstrates how missing values can be effectively replaced with multiple imputations, showcasing the advantages of modern computing in survey analysis.
Readers will find a detailed examination of statistical techniques that benefit researchers analyzing survey data. The text covers both Bayesian and frequentist theories, establishing that standard complete-data methods suffice for analyzing multiply-imputed sets. It evaluates procedures for creating imputations in various scenarios, including ignorable and nonignorable cases, while examples and exercises reinforce the concepts discussed. The interplay between Bayesian and frequentist ideas offers a unified perspective on contemporary statistics.
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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.
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