Nonparametric smoothing in extreme value theory

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.

Grantor dc:publisher.institution

Department of Statistical Sciences

Year dc:date.issued

2010

Author dc:creator

• Clur, John-Craig

Advisor dc:contributor.advisor

• Haines, Linda

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/10285

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/10285