Editing and imputation

All (2) ((2 results))

• 1. Inference for population means under unweighted imputation for missing survey data Archived

  Articles and reports: 12-001-X20030016610

  Description:

  In the presence of item nonreponse, unweighted imputation methods are often used in practice but they generally lead to biased estimators under uniform response within imputation classes. Following Skinner and Rao (2002), we propose a bias-adjusted estimator of a population mean under unweighted ratio imputation and random hot-deck imputation and derive linearization variance estimators. A small simulation study is conducted to study the performance of the methods in terms of bias and mean square error. Relative bias and relative stability of the variance estimators are also studied.

  Release date: 2003-07-31

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• 2. Model explicit item imputation for demographic categories Archived

  Articles and reports: 12-001-X20020026427

  Description:

  We proposed an item imputation method for categorical data based on a Maximum Likelihood Estimator (MLE) derived from a conditional probability model (Besag 1974). We also defined a measure for the item non-response error that was useful in evaluating the bias relative to other imputation methods. To compute this measure, we used Bayesian iterative proportional fitting (Gelman and Rubin 1991; Schafer 1997). We implemented our imputation method for the 1998 dress rehearsal of the Census 2000 in Sacramento, and we used the error measure to compare item imputations between our method and a version of the nearest neighbour hot-deck method (Fay 1999; Chen and Shao 1997, 2000) at aggregate levels. Our results suggest that our method gives additional protection against imputation biases caused by heterogeneities between domains of study, relative to the hot-deck method.

  Release date: 2003-01-29

Data (0) (0 results)

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Analysis (2) ((2 results))

• 1. Inference for population means under unweighted imputation for missing survey data Archived

  Articles and reports: 12-001-X20030016610

  Description:

  In the presence of item nonreponse, unweighted imputation methods are often used in practice but they generally lead to biased estimators under uniform response within imputation classes. Following Skinner and Rao (2002), we propose a bias-adjusted estimator of a population mean under unweighted ratio imputation and random hot-deck imputation and derive linearization variance estimators. A small simulation study is conducted to study the performance of the methods in terms of bias and mean square error. Relative bias and relative stability of the variance estimators are also studied.

  Release date: 2003-07-31

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• 2. Model explicit item imputation for demographic categories Archived

  Articles and reports: 12-001-X20020026427

  Description:

  We proposed an item imputation method for categorical data based on a Maximum Likelihood Estimator (MLE) derived from a conditional probability model (Besag 1974). We also defined a measure for the item non-response error that was useful in evaluating the bias relative to other imputation methods. To compute this measure, we used Bayesian iterative proportional fitting (Gelman and Rubin 1991; Schafer 1997). We implemented our imputation method for the 1998 dress rehearsal of the Census 2000 in Sacramento, and we used the error measure to compare item imputations between our method and a version of the nearest neighbour hot-deck method (Fay 1999; Chen and Shao 1997, 2000) at aggregate levels. Our results suggest that our method gives additional protection against imputation biases caused by heterogeneities between domains of study, relative to the hot-deck method.

  Release date: 2003-01-29

Reference (0) (0 results)

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