Extreme value statistics for censored data with heavy tails under competing risks

Julien Worms (1) , Rym Worms (2)

1 LMV - Laboratoire de Mathématiques de Versailles
2 LAMA - Laboratoire d'Analyse et de Mathématiques Appliquées

This paper addresses the problem of estimating, in the presence of random censoring as well as competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in the heavy-tail case. Asymptotic normality of the proposed estimator (which has the form of an Aalen-Johansen integral, and is the first estimator proposed in this context) is established. A small simulation study exhibits its performances for finite samples. Estimation of extreme quantiles of the cumulative incidence function is also addressed.

Mots clés: Aalen-Johansen estimator; Tail inference; Random censoring; Extreme value index; Competing Risks

Domaines: Autres [stat.ML]

Julien Worms, Rym Worms. Extreme value statistics for censored data with heavy tails under competing risks. Metrika, 2018, 81 (7), pp.849-889. ⟨10.1007/s00184-018-0662-3⟩. ⟨hal-01418370v2⟩