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Title: Bayesian mixture models for the prediction of extreme observations Authors:  Isadora Antoniano-Villalobos - Ca' Foscari University of Venice (Italy) [presenting]
Simone Padoan - Bocconi University (Italy)
Boris Beranger - University of New South Wales (Australia)
Abstract: In many applications with interest in large or extreme observations, usual inferential methods may fail to reproduce the tail behaviour of the variables involved. Recent literature has proposed the use of multivariate extreme value theory to predict an unobserved component of a random vector given large observed values of the rest. This is achieved through the estimation of the angular measure controlling the dependence structure in the tail of the distribution. The idea can be extended and used for the prediction of multiple components at adequately large levels, provided the model used for the angular measure is sufficiently flexible enough to capture complex dependence structures. The use of Bernstein polynomials ensures such flexibility and their interpretation as mixture models allows the use of current trans-dimensional MCMC posterior simulation methods for inference.