Bortot, P. and Coles, S. G. and Tawn, Jonathan A. (2000) The multivariate Gaussian tail model: an application to oceanographic data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 49 (1). pp. 31-49. ISSN 0035-9254Full text not available from this repository.
Optimal design of sea-walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies based on data from several UK locations have revealed an inadequacy of this class for modelling the types of dependence that are often encountered in such data. This paper develops a specific model based on the marginal transformation of the tail of a multivariate Gaussian distribution and examines its utility in overcoming the limitations that are encountered with the current methodology. Diagnostics for the model are developed and the robustness of the model is demonstrated through a simulation study. Our analysis focuses on extreme sea-levels at Newlyn, a port in south-west England, for which previous studies had given conflicting estimates of the probability of flooding. The novel diagnostics suggest that this discrepancy may be due to the weak dependence at extreme levels between wave periods and both wave heights and still water levels. The multivariate Gaussian tail model is shown to resolve the conflict and to offer a convincing description of the extremal sea-state process at Newlyn.
|Journal or Publication Title:||Journal of the Royal Statistical Society: Series C (Applied Statistics)|
|Uncontrolled Keywords:||Asymptotic independence • Extreme value theory • Gaussian distribution • Joint probabilities method • Multivariate extreme value distribution • Oceanography • Structure variable method • Threshold models|
|Subjects:||?? qa ??|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
Faculty of Science and Technology > Lancaster Environment Centre
|Deposited On:||18 Nov 2008 10:19|
|Last Modified:||24 Apr 2017 01:21|
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