## Multiple Imputation for Nonresponse in SurveysDemonstrates 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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approximately assume asymptotic average Bayes's theorem Bayesian inference Chapter complete-data inferences complete-data statistics completed data set components conditional distribution covariates coverage degrees of freedom distribution of Q draw drawn values estimand estimation task evaluated example follow-up followed-up nonrespondents fraction of missing given Sm hot-deck ignorable nonresponse imputation task imputed values interval estimate large samples linear regression matrix mean and variance missing data missing information missing values missingness monotone pattern multiple imputations multiply-imputed data set nominal level nonignorable nonresponse nonre notation observed values parameters population posterior distribution posterior mean posterior probability prior distribution priori independent problem proper imputation methods public-use data bases Q given random variable random-response randomization-based reference distribution repeated imputations repeated-imputation response mechanism Rinc Rubin sampling mechanism Section significance level simple random sample specification standard complete-data standard errors Suppose survey Table tion units univariate Yi values of Yi variance-covariance matrix Yinc Yobs