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Peaks over random threshold methodology for tail index and high quantile estimation

2006-11Journal article Open access
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dc.contributor.authorSantos, Paulo Araújo
dc.contributor.authorAlves, M. Isabel Fraga
dc.contributor.authorGomes, M. Ivette
dc.date.accessioned2010-07-09T13:34:33Z
dc.date.available2010-07-09T13:34:33Z
dc.date.issued2006-11
dc.description.abstractIn this paper we present a class of semi-parametric high quantile estimators which enjoy a desirable property in the presence of linear transformations of the data. Such a feature is in accordance with the empirical counterpart of the theoretical linearity of a quantile χp: χp(δX + λ) = δχp(X) + λ, for any real λ and positive δ. This class of estimators is based on the sample of excesses over a random threshold, originating what we denominate PORT (Peaks Over Random Threshold) methodology. We prove consistency and asymptotic normality of two high quantile estimators in this class, associated with the PORT-estimators for the tail index. The exact performance of the new tail index and quantile PORT-estimators is compared with the original semiparametric estimators, through a simulation study.pt
dc.identifier.citationSANTOS, Paulo Araújo ; ALVES, M. Isabel Fraga ; GOMES, M. Ivette - Peaks over random threshold methodology for tail index and high quantile estimation. Revstat. ISSN 1645-6726. Vol. 4, no. 3 (Nov. 2006), p. 227-247pt
dc.identifier.issn1645-6726
dc.identifier.urihttp://hdl.handle.net/10400.15/123
dc.language.isoengpt
dc.publisherInstituto Nacional de Estatísticapt
dc.relation.publisherversionhttp://www.ine.pt/revstat/pdf/rs060303.pdfpt
dc.subjectHeavy tailspt
dc.subjectHigh quantilespt
dc.subjectSemi-parametric estimationpt
dc.subjectLinear propertypt
dc.subjectSample of excessespt
dc.titlePeaks over random threshold methodology for tail index and high quantile estimationpt
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage247pt
oaire.citation.startPage227pt
oaire.citation.titleRevstatpt
rcaap.rightsopenAccesspt
rcaap.typearticlept

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