text: abstract
  a tail empirical process for heavy-tailed and right-censored data is introduced and its gaussian approximation is established. in this context, a (weighted) new hill-type estimator for
  positive extreme value index is proposed and its consistency and asymptotic normality are
  proved by means of the aforementioned process in the framework of second-order conditions
  of regular variation. in a comparative simulation study, the newly defined estimator is seen
  to perform better than the already existing ones in terms of both bias and mean squared
  error. as a real data example, we apply our estimation procedure to evaluate the tail index
  of the survival time of australian male aids patients. it is noteworthy that our approach
  may also serve to develop other statistics related to the distribution tail such as second-order
  parameter and reduced-bias tail index estimators. furthermore, the proposed tail empirical
  process provides a goodness-of-fit test for pareto-like models under censorship.
  keywords: extreme value index; heavy tails; random censoring; tail empirical process.
  ams 2010 subject classification: 60f17, 62g30, 62g32, 62p05.