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Title: Semiparametric bivariate modelling with flexible extremal dependence Authors:  Manuele Leonelli - IE University (Spain) [presenting]
Abstract: Inference over multivariate tails often requires several assumptions which may affect the assessment of the extreme dependence structure. Models are usually constructed in such a way that extreme components can either be asymptotically dependent or be independent of each other. Recently, there has been an increasing interest in modelling multivariate extremes more flexibly by allowing models to bridge both asymptotic dependence regimes. In this talk, Novel semiparametric approaches are discussed which allow for a variety of dependence patterns, be them extremal or not, by using in a model-based fashion the full dataset. These build on previous work for inference on marginal exceedances over a high, unknown threshold, by combining it with flexible, semiparametric copula specifications to investigate extreme dependence, thus separately modelling marginals and dependence structure. Because of the generality of the approach, bivariate problems are investigated due to computational challenges, but multivariate extensions are readily available. Empirical results suggest that the proposed approaches can provide sound uncertainty statements about the possibility of asymptotic independence. Estimation of functions of interest for extremes is performed via MCMC algorithms. Environmental applications are used to illustrate the methodology.