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Designs conditions for marine structures are typically informed bythreshold-based extreme value analyses of oceanographic variables, in whichexcesses of a high threshold are modelled by a generalized Pareto (GP)distribution. Too low a threshold leads to bias from model mis-specification;raising the threshold increases the variance of estimators: a bias-variancetrade-off. Many existing threshold selection methods do not address thistrade-off directly, but rather aim to select the lowest threshold above whichthe GP model is judged to hold approximately. In this paper Bayesiancross-validation is used to address the trade-off by comparing thresholds basedon predictive ability at extreme levels. Extremal inferences can be sensitiveto the choice of a single threshold. We use Bayesian model-averaging to combineinferences from many thresholds, thereby reducing sensitivity to the choice ofa single threshold. The methodology is applied to significant wave heightdatasets from the northern North Sea and the Gulf of Mexico.

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