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dc.contributor.authorNæss, Arvid
dc.contributor.authorGaidai, Oleg
dc.contributor.authorKarpa, Oleh Ihorovych
dc.date.accessioned2019-10-25T07:30:28Z
dc.date.available2019-10-25T07:30:28Z
dc.date.created2013-07-26T10:48:34Z
dc.date.issued2013
dc.identifier.citationJournal of Probability and Statistics. 2013, 2013 .nb_NO
dc.identifier.issn1687-952X
dc.identifier.urihttp://hdl.handle.net/11250/2624335
dc.description.abstractThis paper details a method for extreme value prediction on the basis of a sampled time series. The method is specifically designed to account for statistical dependence between the sampled data points in a precise manner. In fact, if properly used, the new method will provide statistical estimates of the exact extreme value distribution provided by the data in most cases of practical interest. It avoids the problem of having to decluster the data to ensure independence, which is a requisite component in the application of, for example, the standard peaks-over-threshold method. The proposed method also targets the use of subasymptotic data to improve prediction accuracy. The method will be demonstrated by application to both synthetic and real data. From a practical point of view, it seems to perform better than the POT and block extremes methods, and, with an appropriate modification, it is directly applicable to nonstationary time series.nb_NO
dc.language.isoengnb_NO
dc.publisherHindawinb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleEstimation of Extreme Values by the Average Conditional Exceedance Rate Methodnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber15nb_NO
dc.source.volume2013nb_NO
dc.source.journalJournal of Probability and Statisticsnb_NO
dc.identifier.doi10.1155/2013/797014
dc.identifier.cristin1040470
dc.description.localcodeCopyright © 2013 A. Naess et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.nb_NO
cristin.unitcode194,64,20,0
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for marin teknikk
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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