Dimension reduction in multivariate extreme value analysis

(1.21) Cette construction peut ˆ etre vue dans le cadre plus g´ en´ eral de la th´ eorie des valeurs extrˆ emes multivari´ ees comme le produit entre une composante radiale et

Three classes of multivariate extreme value distributions are scrutinised: the well-known model obtained from discrete spectral measures, the generalized logistic model and

• Le Query By Committee est une approche qui s’est avérée très prometteuse dans notre contexte des valeurs extrêmes, ceci dit son efficacité a été illustrée dans le Chapitre

In such a difficult context, this paper shows how classification based on Reproducing Kernel Hilbert Space (RKHS) can be used in conjunction with Extreme Value Theory (EVT) to

Their asymptotic normality is derived under quite natural and general extreme value conditions, without Lipschitz conditions on the boundary and without recourse to assumptions

Based on these data in the max-domain of attraction of the logistic distribution we estimate the dependence parameter θ using our Bayes estimator and compare it to the

Emphasizing the Peak-over-Threshold approach that ap- proximates the distribution above high threshold by the Generalized Pareto distribution, we compare the empirical distribution

Our results are based on the fact that the general conditions of multiple mixing and an- ticlustering used in extreme value theory of stationary processes (see [2], [7]) are valid