Functional kernel estimators of conditional extreme quantiles
Statistics Theory
2012-12-07 v1 Statistics Theory
Abstract
We address the estimation of "extreme" conditional quantiles i.e. when their order converges to one as the sample size increases. Conditions on the rate of convergence of their order to one are provided to obtain asymptotically Gaussian distributed kernel estimators. A Weissman-type estimator and kernel estimators of the conditional tail-index are derived, permitting to estimate extreme conditional quantiles of arbitrary order.
Cite
@article{arxiv.1212.1076,
title = {Functional kernel estimators of conditional extreme quantiles},
author = {L. Gardes and S. Girard},
journal= {arXiv preprint arXiv:1212.1076},
year = {2012}
}
Comments
arXiv admin note: text overlap with arXiv:1107.2261