Extreme value statistics for censored data with heavy tails under competing risks
Statistics Theory
2017-01-20 v1 Machine Learning
Statistics Theory
Abstract
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.
Cite
@article{arxiv.1701.05458,
title = {Extreme value statistics for censored data with heavy tails under competing risks},
author = {Julien Worms and Rym Worms},
journal= {arXiv preprint arXiv:1701.05458},
year = {2017}
}