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Cross-validatory extreme value threshold selection and uncertainty with application to ocean storm severity

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<mark>Journal publication date</mark>1/01/2017
<mark>Journal</mark>Journal of the Royal Statistical Society: Series C (Applied Statistics)
Issue number1
Number of pages28
Pages (from-to)93-120
Publication StatusPublished
Early online date20/05/16
<mark>Original language</mark>English


Design conditions for marine structures are typically informed by threshold-based extreme value analyses of oceanographic variables, in which excesses of a high threshold are modelled by a generalized Pareto distribution. Too low a threshold leads to bias from model misspecification, and raising the threshold increases the variance of estimators: a bias–variance trade-off. Many existing threshold selection methods do not address this trade-off directly but rather aim to select the lowest threshold above which the generalized Pareto model is judged to hold approximately. In the paper Bayesian cross-validation is used to address the trade-off by comparing thresholds based on predictive ability at extreme levels. Extremal inferences can be sensitive to the choice of a single threshold. We use Bayesian model averaging to combine inferences from many thresholds, thereby reducing sensitivity to the choice of a single threshold. The methodology is applied to significant wave height data sets from the northern North Sea and the Gulf of Mexico. © 2016 Royal Statistical Society