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A hierarchical model for extreme wind speeds

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A typical extreme value analysis is often carried out on the basis of simplistic inferential procedures, though the data being analysed may be structurally complex. Here we develop a hierarchical model for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location. A Gaussian model for the random effects exploits the meteorological structure in the data, enabling increased precision for inferences at individual sites and in individual seasons. The Bayesian framework that is adopted is also exploited to obtain predictive return level estimates at each site, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes that are involved.

Keywords: Extreme value theory; Generalized Pareto distribution; Hierarchical model; Markov chain; Wind speeds

Document Type: Research Article


Affiliations: University of Newcastle, UK

Publication date: November 1, 2006


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