Learning the dependence structure of rare events: a non-asymptotic study


Nicolas Goix, Anne Sabourin, Stéphan Clémen\ccon ;
Proceedings of The 28th Conference on Learning Theory, PMLR 40:843-860, 2015.


Assessing the probability of occurrence of extreme events is a crucial issue in various fields like finance, insurance, telecommunication or environmental sciences. In a multivariate framework, the tail dependence is characterized by the so-called \emphstable tail dependence function (\textscstdf). Learning this structure is the keystone of multivariate extremes. Although extensive studies have proved consistency and asymptotic normality for the empirical version of the \textscstdf, non-asymptotic bounds are still missing. The main purpose of this paper is to fill this gap. Taking advantage of adapted VC-type concentration inequalities, upper bounds are derived with expected rate of convergence in O(k^-1/2). The concentration tools involved in this analysis rely on a more general study of maximal deviations in low probability regions, and thus directly apply to the classification of extreme data.

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