Combining data from two independent surveys: a model-assisted approach

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dc.contributor.authorKim, Jae Kwangko
dc.contributor.authorRao, J. N. K.ko
dc.date.accessioned2016-09-08T00:51:50Z-
dc.date.available2016-09-08T00:51:50Z-
dc.date.created2016-09-07-
dc.date.created2016-09-07-
dc.date.issued2012-03-
dc.identifier.citationBIOMETRIKA, v.99, no.1, pp.85 - 100-
dc.identifier.issn0006-3444-
dc.identifier.urihttp://hdl.handle.net/10203/212937-
dc.description.abstractCombining information from two or more independent surveys is a problem frequently encountered in survey sampling. We consider the case of two independent surveys, where a large sample from survey 1 collects only auxiliary information and a much smaller sample from survey 2 provides information on both the variables of interest and the auxiliary variables. We propose a model-assisted projection method of estimation based on a working model, but the reference distribution is design-based. We generate synthetic or proxy values of a variable of interest by first fitting the working model, relating the variable of interest to the auxiliary variables, to the data from survey 2 and then predicting the variable of interest associated with the auxiliary variables observed in survey 1. The projection estimator of a total is simply obtained from the survey 1 weights and associated synthetic values. We identify the conditions for the projection estimator to be asymptotically unbiased. Domain estimation using the projection method is also considered. Replication variance estimators are obtained by augmenting the synthetic data file for survey 1 with additional synthetic columns associated with the columns of replicate weights. Results from a simulation study are presented-
dc.languageEnglish-
dc.publisherOXFORD UNIV PRESS-
dc.subjectMULTIPLE SURVEYS-
dc.subjectINFORMATION-
dc.subjectREGRESSION-
dc.subjectESTIMATORS-
dc.subjectIMPUTATION-
dc.subjectSUPERPOPULATION-
dc.subjectERROR-
dc.titleCombining data from two independent surveys: a model-assisted approach-
dc.typeArticle-
dc.identifier.wosid000300734400007-
dc.identifier.scopusid2-s2.0-84863249206-
dc.type.rimsART-
dc.citation.volume99-
dc.citation.issue1-
dc.citation.beginningpage85-
dc.citation.endingpage100-
dc.citation.publicationnameBIOMETRIKA-
dc.identifier.doi10.1093/biomet/asr063-
dc.contributor.localauthorKim, Jae Kwang-
dc.contributor.nonIdAuthorRao, J. N. K.-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorDouble sampling-
dc.subject.keywordAuthorMass imputation-
dc.subject.keywordAuthorSynthetic data-
dc.subject.keywordAuthorTwo-phase sampling-
dc.subject.keywordPlusMULTIPLE SURVEYS-
dc.subject.keywordPlusINFORMATION-
dc.subject.keywordPlusREGRESSION-
dc.subject.keywordPlusESTIMATORS-
dc.subject.keywordPlusIMPUTATION-
dc.subject.keywordPlusSUPERPOPULATION-
dc.subject.keywordPlusERROR-
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