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dc.contributor.author이동환-
dc.date.accessioned2020-12-23T16:30:04Z-
dc.date.available2020-12-23T16:30:04Z-
dc.date.issued2020-
dc.identifier.issn0962-2802-
dc.identifier.issn1477-0334-
dc.identifier.otherOAK-27838-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/255783-
dc.description.abstractIn clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases.-
dc.languageEnglish-
dc.publisherSAGE PUBLICATIONS LTD-
dc.subjectClustering-
dc.subjectfalse assignment rate-
dc.subjectextended likelihood-
dc.titleClustering with varying risks of false assignments in discrete latent variable model-
dc.typeArticle-
dc.relation.issue10-
dc.relation.volume29-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage2932-
dc.relation.lastpage2944-
dc.relation.journaltitleSTATISTICAL METHODS IN MEDICAL RESEARCH-
dc.identifier.doi10.1177/0962280220913067-
dc.identifier.wosidWOS:000523544800001-
dc.identifier.scopusid2-s2.0-85082937867-
dc.author.googleLee, Donghwan-
dc.author.googleChoi, Dongseok-
dc.author.googleLee, Youngjo-
dc.contributor.scopusid이동환(56434427300)-
dc.date.modifydate20201223140200-
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