Nonresponse weighting adjustment using estimated response probability

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dc.contributor.authorKim, Jae Kwangko
dc.contributor.authorKim, Jay J.ko
dc.date.accessioned2016-09-08T00:53:13Z-
dc.date.available2016-09-08T00:53:13Z-
dc.date.created2016-09-07-
dc.date.created2016-09-07-
dc.date.issued2007-12-
dc.identifier.citationCANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, v.35, no.4, pp.501 - 514-
dc.identifier.issn0319-5724-
dc.identifier.urihttp://hdl.handle.net/10203/212949-
dc.description.abstractTo reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator-
dc.languageEnglish-
dc.publisherCANADIAN JOURNAL STATISTICS-
dc.subjectMODEL-
dc.subjectIMPUTATION-
dc.titleNonresponse weighting adjustment using estimated response probability-
dc.typeArticle-
dc.identifier.wosid000253024800003-
dc.identifier.scopusid2-s2.0-40549127811-
dc.type.rimsART-
dc.citation.volume35-
dc.citation.issue4-
dc.citation.beginningpage501-
dc.citation.endingpage514-
dc.citation.publicationnameCANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE-
dc.contributor.localauthorKim, Jae Kwang-
dc.contributor.nonIdAuthorKim, Jay J.-
dc.type.journalArticleArticle-
dc.subject.keywordAuthornonresponse-
dc.subject.keywordAuthorpropensity score-
dc.subject.keywordAuthorregression estimator-
dc.subject.keywordAuthorsurvey sampling-
dc.subject.keywordAuthorunit nonresponse-
dc.subject.keywordAuthorvariance estimation-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusIMPUTATION-
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