Nonparametric smoothing in extreme value theory

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dc.contributor.advisorHaines, Lindaen_ZA
dc.contributor.authorClur, John-Craigen_ZA
dc.date.accessioned2014-12-27T19:45:40Z
dc.date.available2014-12-27T19:45:40Z
dc.date.issued2010en_ZA
dc.descriptionIncludes bibliographical references (leaves 137-138).en_ZA
dc.description.abstractThis work investigates the modelling of non-stationary sample extremes using a roughness penalty approach, in which smoothed natural cubic splines are fitted to the location and scale parameters of the generalized extreme value distribution and the distribution of the r largest order statistics. Estimation is performed by implementing a Fisher scoring algorithm to maximize the penalized log-likelihood function. The approach provides a flexible framework for exploring smooth trends in sample extremes, with the benefit of balancing the trade-off between 'smoothness' and adherence to the underlying data by simply changing the smoothing parameter. To evaluate the overall performance of the extreme value theory methodology in smoothing extremes a simulation study was performed.en_ZA
dc.identifier.apacitationClur, J. (2010). <i>Nonparametric smoothing in extreme value theory</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/10285en_ZA
dc.identifier.chicagocitationClur, John-Craig. <i>"Nonparametric smoothing in extreme value theory."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2010. http://hdl.handle.net/11427/10285en_ZA
dc.identifier.citationClur, J. 2010. Nonparametric smoothing in extreme value theory. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Clur, John-Craig AB - This work investigates the modelling of non-stationary sample extremes using a roughness penalty approach, in which smoothed natural cubic splines are fitted to the location and scale parameters of the generalized extreme value distribution and the distribution of the r largest order statistics. Estimation is performed by implementing a Fisher scoring algorithm to maximize the penalized log-likelihood function. The approach provides a flexible framework for exploring smooth trends in sample extremes, with the benefit of balancing the trade-off between 'smoothness' and adherence to the underlying data by simply changing the smoothing parameter. To evaluate the overall performance of the extreme value theory methodology in smoothing extremes a simulation study was performed. DA - 2010 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2010 T1 - Nonparametric smoothing in extreme value theory TI - Nonparametric smoothing in extreme value theory UR - http://hdl.handle.net/11427/10285 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/10285
dc.identifier.vancouvercitationClur J. Nonparametric smoothing in extreme value theory. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2010 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/10285en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherFinancial Mathematicsen_ZA
dc.titleNonparametric smoothing in extreme value theoryen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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