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Bayesian inference for clustered extremes

Lookup NU author(s): Dr Lee Fawcett, Dr David Walshaw

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Abstract

We consider Bayesian inference for the extremes of dependent stationary series. We discuss the virtues of the Bayesian approach to inference for the extremal index, and for related characteristics of clustering behaviour. We develop an inference procedure based on an automatic declustering scheme, and using simulated data we implement and assess this procedure, making inferences for the extremal index, and for two cluster functionals. We then apply our procedure to a set of real data, specifically a time series of wind-speed measurements, where the clusters correspond to storms. Here the two cluster functionals selected previously correspond to the mean storm length and the mean inter-storm interval. We also consider inference for long-period return levels, advocating the posterior predictive distribution as being most representative of the information required by engineers interested in design level specifications. © 2007 Springer Science+Business Media, LLC.


Publication metadata

Author(s): Fawcett L, Walshaw D

Publication type: Article

Publication status: Published

Journal: Extremes

Year: 2008

Volume: 11

Issue: 3

Pages: 217-233

Print publication date: 01/09/2008

ISSN (print): 13861999

ISSN (electronic): 1572-915X

Publisher: Springer New York LLC

URL: http://dx.doi.org/10.1007/s10687-007-0054-y

DOI: 10.1007/s10687-007-0054-y


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