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The modeling of dependence between maxima is an important subject in severalapplications in risk analysis. To this aim, the extreme value copula function,characterised via the madogram, can be used as a margin-free description of thedependence structure. From a practical point of view, the family of extremevalue distributions is very rich and arise naturally as the limitingdistribution of properly normalised component-wise maxima. In this paper, weinvestigate the nonparametric estimation of the madogram where data arecompletely missing at random. We provide the functional central limit theoremfor the considered multivariate madrogram correctly normalized, towards a tightGaussian process for which the covariance function depends on the probabilitiesof missing. Explicit formula for the asymptotic variance is also given. Ourresults are illustrated in a finite sample setting with a simulation study.

Non-parametric estimator of a multivariate madogram for missing-data and extreme value framework