Lookup NU author(s): Dr Lee Fawcett,
Dr David Walshaw
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
Author(s): Fawcett L, Walshaw D
Publication type: Article
Publication status: Published
Print publication date: 01/09/2008
ISSN (print): 13861999
ISSN (electronic): 1572-915X
Publisher: Springer New York LLC
Altmetrics provided by Altmetric