Lancaster EPrints

Modelling non-stationary extremes with application to surface level ozone.

Eastoe, Emma F. and Tawn, Jonathan A. (2009) Modelling non-stationary extremes with application to surface level ozone. Journal of the Royal Statistical Society: Series C (Applied Statistics), 58 (1). pp. 25-45. ISSN 0035-9254

Full text not available from this repository.

Abstract

Statistical methods for modelling extremes of stationary sequences have received much attention. The most common method is to model the rate and size of exceedances of some high constant threshold; the size of exceedances is modelled by using a generalized Pareto distribution. Frequently, data sets display non-stationarity; this is especially common in environmental applications. The ozone data set that is presented here is an example of such a data set. Surface level ozone levels display complex seasonal patterns and trends due to the mechanisms that are involved in ozone formation. The standard methods of modelling the extremes of a non-stationary process focus on retaining a constant threshold but using covariate models in the rate and generalized Pareto distribution parameters. We suggest an alternative approach that uses preprocessing methods to model the non-stationarity in the body of the process and then uses standard methods to model the extremes of the preprocessed data. We illustrate both the standard and the preprocessing methods by using a simulation study and a study of the ozone data. We suggest that the preprocessing method gives a model that better incorporates the underlying mechanisms that generate the process, produces a simpler and more efficient fit and allows easier computation.

Item Type: Article
Journal or Publication Title: Journal of the Royal Statistical Society: Series C (Applied Statistics)
Uncontrolled Keywords: Generalized Pareto distribution • Non-stationary process • Ozone • Preprocessing • Return levels • Threshold exceedances
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 26850
Deposited By: Dr Emma Eastoe
Deposited On: 31 Jul 2009 10:02
Refereed?: Yes
Published?: Published
Last Modified: 28 Oct 2014 09:12
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/26850

Actions (login required)

View Item