An efficient method of estimation for longitudinal surveys with monotone missing data

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dc.contributor.authorZhou, Mingko
dc.contributor.authorKim, Jae Kwangko
dc.date.accessioned2016-10-04T02:58:44Z-
dc.date.available2016-10-04T02:58:44Z-
dc.date.created2016-09-08-
dc.date.created2016-09-08-
dc.date.issued2012-09-
dc.identifier.citationBIOMETRIKA, v.99, no.3, pp.631 - 648-
dc.identifier.issn0006-3444-
dc.identifier.urihttp://hdl.handle.net/10203/213010-
dc.description.abstractPanel attrition is frequently encountered in panel sample surveys. When it is related to the observed study variable, the classical approach of nonresponse adjustment using a covariate-dependent dropout mechanism can be biased. We consider an efficient method of estimation with monotone panel attrition when the response probability depends on the previous values of study variable as well as other covariates. Because of the monotone structure of the missing pattern, the response mechanism is missing at random. The proposed estimator is asymptotically optimal in the sense that it minimizes the asymptotic variance of a class of estimators that can be written as a linear combination of the unbiased estimators of the panel estimates for each wave, and incorporates all available information using generalized least squares. Variance estimation is discussed and results from a simulation study are presented-
dc.languageEnglish-
dc.publisherOXFORD UNIV PRESS-
dc.subjectFOOD-STAMP PARTICIPATION-
dc.subjectVARIANCE-ESTIMATION-
dc.subjectSEMIPARAMETRIC REGRESSION-
dc.subjectREPEATED OUTCOMES-
dc.subjectRESPONSE ERROR-
dc.subjectDROP-OUT-
dc.subjectNONRESPONSE-
dc.subjectPARAMETERS-
dc.subjectADJUSTMENT-
dc.subjectSTATISTICS-
dc.titleAn efficient method of estimation for longitudinal surveys with monotone missing data-
dc.typeArticle-
dc.identifier.wosid000308019700008-
dc.identifier.scopusid2-s2.0-84865449312-
dc.type.rimsART-
dc.citation.volume99-
dc.citation.issue3-
dc.citation.beginningpage631-
dc.citation.endingpage648-
dc.citation.publicationnameBIOMETRIKA-
dc.identifier.doi10.1093/biomet/ass026-
dc.contributor.localauthorKim, Jae Kwang-
dc.contributor.nonIdAuthorZhou, Ming-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorGeneralized least squares-
dc.subject.keywordAuthorMissingness at random-
dc.subject.keywordAuthorPanel attrition-
dc.subject.keywordAuthorPropensity score-
dc.subject.keywordAuthorSurvey in time-
dc.subject.keywordPlusFOOD-STAMP PARTICIPATION-
dc.subject.keywordPlusVARIANCE-ESTIMATION-
dc.subject.keywordPlusSEMIPARAMETRIC REGRESSION-
dc.subject.keywordPlusREPEATED OUTCOMES-
dc.subject.keywordPlusRESPONSE ERROR-
dc.subject.keywordPlusDROP-OUT-
dc.subject.keywordPlusNONRESPONSE-
dc.subject.keywordPlusPARAMETERS-
dc.subject.keywordPlusADJUSTMENT-
dc.subject.keywordPlusSTATISTICS-
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