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Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation

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  • Reiter, Jerome P.

Abstract

Multiple imputation can handle missing data and disclosure limitation simultaneously. First, fill in the missing data to generate m completed datasets, then replace confidential values in each completed dataset with r imputations. I investigate how to select m and r.

Suggested Citation

  • Reiter, Jerome P., 2008. "Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation," Statistics & Probability Letters, Elsevier, vol. 78(1), pages 15-20, January.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:1:p:15-20
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    References listed on IDEAS

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    1. Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205, January.
    2. John M. Abowd & Simon D. Woodcock, 2004. "Multiply-Imputing Confidential Characteristics and File Links in Longitudinal Linked Data," Longitudinal Employer-Household Dynamics Technical Papers 2004-04, Center for Economic Studies, U.S. Census Bureau.
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    Cited by:

    1. Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.

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