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dc.contributor.authorGebizlioğlu, Ömer L.
dc.contributor.authorŞenoğlu, Birdal
dc.contributor.authorMert Kantar, Yeliz
dc.date.accessioned2019-10-20T09:31:19Z
dc.date.available2019-10-20T09:31:19Z
dc.date.issued2011
dc.identifier.issn0377-0427
dc.identifier.urihttps://dx.doi.org/10.1016/j.cam.2011.01.044
dc.identifier.urihttps://hdl.handle.net/11421/17664
dc.descriptionWOS: 000290067100005en_US
dc.description.abstractThe Weibull distribution is one of the most important distributions that is utilized as a probability model for loss amounts in connection with actuarial and financial risk management problems. This paper considers the Weibull distribution and its quantiles in the context of estimation of a risk measure called Value-at-Risk (VaR). VaR is simply the maximum loss in a specified period with a pre-assigned probability level. We attempt to present certain estimation methods for VaR as a quantile of a distribution and compare these methods with respect to their deficiency (Def) values. Along this line, the results of some Monte Carlo simulations, that we have conducted for detailed investigations on the efficiency of the estimators as compared to MLE, are provideden_US
dc.language.isoengen_US
dc.publisherElsevier Science BVen_US
dc.relation.isversionof10.1016/j.cam.2011.01.044en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectValue-At-Risken_US
dc.subjectQuantilesen_US
dc.subjectWeibull Distributionen_US
dc.subjectMonte Carlo Simulationen_US
dc.subjectDeficiencyen_US
dc.titleComparison of certain value-at-risk estimation methods for the two-parameter Weibull loss distributionen_US
dc.typearticleen_US
dc.relation.journalJournal of Computational and Applied Mathematicsen_US
dc.contributor.departmentAnadolu Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.identifier.volume235en_US
dc.identifier.issue11en_US
dc.identifier.startpage3304en_US
dc.identifier.endpage3314en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorMert Kantar, Yeliz


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