Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/30002
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dc.contributor.authorGomes, M. Ivettept_PT
dc.contributor.authorMiranda, M. Cristinapt_PT
dc.contributor.authorMiranda, Manuela Souto dept_PT
dc.date.accessioned2020-12-11T14:41:17Z-
dc.date.available2020-12-11T14:41:17Z-
dc.date.issued2020-12-
dc.identifier.isbn978-3-030-57305-8pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/30002-
dc.description.abstractMany examples in the most diverse fields of application show the need for statistical methods of analysis of extremes of dependent data. A crucial issue that appears when there is dependency is the reliable estimation of the extremal index (EI), a parameter related to the clustering of large events. The most popular EI-estimators, like the blocks’ EI-estimators, are very sensitive to anomalous cluster sizes and exhibit a high bias. The need for robust versions of such EI-estimators is the main topic under discussion in this paper.pt_PT
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationUID/MAT/00006/2019pt_PT
dc.relationUID/MAT/04106/2019pt_PT
dc.rightsrestrictedAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDependent sequencespt_PT
dc.subjectMonte-Carlo simulationpt_PT
dc.subjectRobust semi-parametric estimationpt_PT
dc.subjectStatistics of extremespt_PT
dc.titleA note on robust estimation of the extremal indexpt_PT
dc.typebookPartpt_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage213pt_PT
degois.publication.lastPage225pt_PT
degois.publication.locationSuíçapt_PT
degois.publication.titleNonparametric Statisticspt_PT
dc.identifier.doi10.1007/978-3-030-57306-5_20pt_PT
dc.identifier.esbn978-3-030-57306-5pt_PT
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