Parametric fractional imputation for missing data analysis Kim, Jae Kwang Kim, Jae Kwang
dc.contributor.department Statistics 2018-02-18T16:59:06.000 2020-07-02T06:56:32Z 2020-07-02T06:56:32Z Sat Jan 01 00:00:00 UTC 2011 2011-01-01
dc.description.abstract <p>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.</p>
dc.description.comments <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="" target="_blank">10.1093/biomet/asq073</a>. Posted with permission.</p>
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dc.identifier.articleid 1121
dc.identifier.contextkey 10458008
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dc.source.bitstream archive/|||Fri Jan 14 18:15:59 UTC 2022
dc.source.uri 10.1093/biomet/asq073
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistics and Probability
dc.subject.keywords EM algorithm
dc.subject.keywords Importance sampling
dc.subject.keywords Item nonresponse
dc.subject.keywords Monte Carlo EM
dc.subject.keywords Multiple imputation
dc.title Parametric fractional imputation for missing data analysis
dc.type article
dc.type.genre article
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