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Name: Stan Tendijck 

Talk Title: Modelling the extremes of bivariate mixture distributions with application to oceanographic data

Abstract: There currently exist a variety of statistical methods for modelling bivariate extremes. However, when the dependence between variables is driven by more than one latent process, these methods are likely to fail to give reliable inferences. We consider situations in which the observed dependence at extreme levels is a mixture of a possibly unknown number of much simpler bivariate distributions. For such structures we demonstrate the limitations of existing methods and we propose two new methods: an extension of the Heffernan-Tawn conditional extreme value model to allow for mixtures and an extremal quantile-regression approach. The two methods are examined in a simulation study and are applied to oceanographic data. We discuss extensions including a subasymptotic version of the proposed model, which has the potential to give more efficient results by incorporating data that are less extreme. Both of the new methods outperform existing approaches when mixtures are present. The quantile-regression model performs better than the extension of the Heffernan-Tawn conditional extreme value model when the number of mixture components is unknown, and the methods are comparable when this number is known.

This talk is a contributed talk at EVA 2021. View the programme here.