Parametric fractional imputation for missing data analysis

Date
2011-01-01
Authors
Kim, Jae Kwang
Kim, Jae Kwang
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Abstract

Parametric fractional imputation is proposed as a general tool for missing data analysis. Using fractional weights, the observed likelihood can be approximated by the weighted mean of the imputed data likelihood. Computational efficiency can be achieved using the idea of importance sampling and calibration weighting. The proposed imputation method provides efficient parameter estimates for the model parameters specified in the imputation model and also provides reasonable estimates for parameters that are not part of the imputation model. Variance estimation is discussed and results from a limited simulation study are presented.

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<p>This is a pre-copyedited, author-produced PDF of an article submitted for publication in <em>Biometrika</em>. The version of record (Kim, Jae Kwang. "Parametric fractional imputation for missing data analysis." <em>Biometrika</em> 98, no. 1 (2011): 119-132) is available online at doi:<a href="http://dx.doi.org/10.1093/biomet/asq073" target="_blank">10.1093/biomet/asq073</a>. Posted with permission.</p>
Keywords
EM algorithm, Importance sampling, Item nonresponse, Monte Carlo EM, Multiple imputation
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