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• 1. Variance estimation after imputation Archived

  Articles and reports: 12-001-X20010015856

  Description:

  Imputation is commonly used to compensate for item nonresponse. Variance estimation after imputation has generated considerable discussion and several variance estimators have been proposed. We propose a variance estimator based on a pseudo data set used only for variance estimation. Standard complete data variance estimators applied to the pseudo data set lead to consistent estimators for linear estimators under various imputation methods, including without-replacement hot deck imputation and with-replacement hot deck imputation. The asymptotic equivalence of the proposed method and the adjusted jackknife method of Rao and Sitter (1995) is illustrated. The proposed method is directly applicable to variance estimation for two-phase sampling.

  Release date: 2001-08-22

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• 2. A multivariate technique for multiply imputing missing values using a sequence of regression models Archived

  Articles and reports: 12-001-X20010015857

  Description:

  This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.

  Release date: 2001-08-22

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• 1. Variance estimation after imputation Archived

  Articles and reports: 12-001-X20010015856

  Description:

  Imputation is commonly used to compensate for item nonresponse. Variance estimation after imputation has generated considerable discussion and several variance estimators have been proposed. We propose a variance estimator based on a pseudo data set used only for variance estimation. Standard complete data variance estimators applied to the pseudo data set lead to consistent estimators for linear estimators under various imputation methods, including without-replacement hot deck imputation and with-replacement hot deck imputation. The asymptotic equivalence of the proposed method and the adjusted jackknife method of Rao and Sitter (1995) is illustrated. The proposed method is directly applicable to variance estimation for two-phase sampling.

  Release date: 2001-08-22

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• 2. A multivariate technique for multiply imputing missing values using a sequence of regression models Archived

  Articles and reports: 12-001-X20010015857

  Description:

  This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.

  Release date: 2001-08-22

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