Abstract
Abstract Multiple imputation was designed to handle the problem of missing data in public-use data bases where the data-base constructor and the ultimate user are distinct entities. The objective is valid frequency inference for ultimate users who in general have access only to complete-data software and possess limited knowledge of specific reasons and models for nonresponse. For this situation and objective, I believe that multiple imputation by the data-base constructor is the method of choice. This article first provides a description of the assumed context and objectives, and second, reviews the multiple imputation framework and its standard results. These preliminary discussions are especially important because some recent commentaries on multiple imputation have reflected either misunderstandings of the practical objectives of multiple imputation or misunderstandings of fundamental theoretical results. Then, criticisms of multiple imputation are considered, and, finally, comparisons are made to alternative strategies.
Keywords
Imputation (statistics)Computer scienceInferenceMissing dataData miningSoftwareData scienceInformation retrievalArtificial intelligenceMachine learning
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 91
- Issue
- 434
- Pages
- 473-489
- Citations
- 2846
- Access
- Closed
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Cite This
Donald B. Rubin
(1996).
Multiple Imputation after 18+ Years.
Journal of the American Statistical Association
, 91
(434)
, 473-489.
https://doi.org/10.1080/01621459.1996.10476908
Identifiers
- DOI
- 10.1080/01621459.1996.10476908