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Maximum composite likelihood estimation for spatial extremes models of Brown-Resnick type with application to precipitation data

DC Field Value Language
dc.contributor.authorKim, Moosup-
dc.contributor.authorLee, Sangyeol-
dc.date.accessioned2022-05-04T02:06:54Z-
dc.date.available2022-05-04T02:06:54Z-
dc.date.created2021-08-26-
dc.date.issuedACCEPT-
dc.identifier.citationScandinavian Journal of Statistics-
dc.identifier.issn0303-6898-
dc.identifier.urihttps://hdl.handle.net/10371/179513-
dc.description.abstractIn this study, we consider the maximum composite likelihood estimator for spatial extremes model class of Brown-Resnick type. The composite likelihood is constructed based on the weighted tail empirical process. It is shown that the proposed estimator is consistent and asymptotically normal under some regularity conditions fulfilled by the model class. We conduct Monte Carlo simulations to evaluate the estimator and apply it to the analysis of a precipitation data set.-
dc.language영어-
dc.publisherBlackwell Publishing Inc.-
dc.titleMaximum composite likelihood estimation for spatial extremes models of Brown-Resnick type with application to precipitation data-
dc.typeArticle-
dc.identifier.doi10.1111/sjos.12551-
dc.citation.journaltitleScandinavian Journal of Statistics-
dc.identifier.wosid000684954800001-
dc.identifier.scopusid2-s2.0-85112368944-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorLee, Sangyeol-
dc.type.docTypeArticle; Early Access-
dc.description.journalClass1-
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