Extreme value statistics for censored data with heavy tails under competing risks
  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 first estimator proposed in this
context) is established. A small simulation study exhibits its performances for
finite samples. Estimation of extreme quantiles of the cumulative incidence
function is also addressed.
