Nonparametric kernel estimation of Weibull-tail coefficient in presence of the right random censoring
Methodology
2021-10-12 v1 Statistics Theory
Computation
Statistics Theory
Abstract
In this paper, nonparametric estimation of the conditional Weibull-tail coefficient when the variable of interest is right random censored is addressed. A Weissman-type estimator of conditional extreme quantile is also proposed. In addition, a simulation study is conducted to assess the finite-sample behavior of the proposed estimators and a comparison with alternative strategies is provided. Finally, the practical applicability of the methodology is presented using a real datasets of men suffering from a larynx cancer.
Keywords
Cite
@article{arxiv.2110.04772,
title = {Nonparametric kernel estimation of Weibull-tail coefficient in presence of the right random censoring},
author = {Justin Ushize Rutikange and Aliou Diop},
journal= {arXiv preprint arXiv:2110.04772},
year = {2021}
}