Parametric fractional imputation for missing data analysis

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dc.contributor.authorKim, Jae Kwangko
dc.date.accessioned2016-09-08T00:52:10Z-
dc.date.available2016-09-08T00:52:10Z-
dc.date.created2016-09-07-
dc.date.created2016-09-07-
dc.date.issued2011-03-
dc.identifier.citationBIOMETRIKA, v.98, no.1, pp.119 - 132-
dc.identifier.issn0006-3444-
dc.identifier.urihttp://hdl.handle.net/10203/212940-
dc.description.abstractParametric 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-
dc.languageEnglish-
dc.publisherOXFORD UNIV PRESS-
dc.subjectEM ALGORITHM-
dc.subjectINCOMPLETE DATA-
dc.subjectINFERENCE-
dc.subjectLIKELIHOOD-
dc.subjectMODELS-
dc.titleParametric fractional imputation for missing data analysis-
dc.typeArticle-
dc.identifier.wosid000287759000009-
dc.identifier.scopusid2-s2.0-79952165896-
dc.type.rimsART-
dc.citation.volume98-
dc.citation.issue1-
dc.citation.beginningpage119-
dc.citation.endingpage132-
dc.citation.publicationnameBIOMETRIKA-
dc.identifier.doi10.1093/biomet/asq073-
dc.contributor.localauthorKim, Jae Kwang-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorem algorithm-
dc.subject.keywordAuthorImportance sampling-
dc.subject.keywordAuthorItem nonresponse-
dc.subject.keywordAuthorMonte Carlo EM-
dc.subject.keywordAuthorMultiple imputation-
dc.subject.keywordPlusEM ALGORITHM-
dc.subject.keywordPlusINCOMPLETE DATA-
dc.subject.keywordPlusINFERENCE-
dc.subject.keywordPlusLIKELIHOOD-
dc.subject.keywordPlusMODELS-
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