Confidence regions for high quantiles of a heavy tailed distribution
Statistics Theory
2007-06-13 v1 Statistics Theory
Abstract
Estimating high quantiles plays an important role in the context of risk management. This involves extrapolation of an unknown distribution function. In this paper we propose three methods, namely, the normal approximation method, the likelihood ratio method and the data tilting method, to construct confidence regions for high quantiles of a heavy tailed distribution. A simulation study prefers the data tilting method.
Keywords
Cite
@article{arxiv.math/0611278,
title = {Confidence regions for high quantiles of a heavy tailed distribution},
author = {Liang Peng and Yongcheng Qi},
journal= {arXiv preprint arXiv:math/0611278},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/009053606000000416 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)