Elemental unbiased estimators for the Generalized Pareto tail
Statistics Theory
2013-04-16 v1 Statistics Theory
Abstract
Unbiased location- and scale-invariant `elemental' estimators for the GPD tail parameter are constructed. Each involves three log-spacings. The estimators are unbiased for finite sample sizes, even as small as N=3. It is shown that the elementals form a complete basis for unbiased location- and scale-invariant estimators constructed from linear combinations of log-spacings. Preliminary numerical evidence is presented which suggests that elemental combinations can be constructed which are consistent estimators of the tail parameter for samples drawn from the pure GPD family.
Cite
@article{arxiv.1304.3918,
title = {Elemental unbiased estimators for the Generalized Pareto tail},
author = {Allan McRobie},
journal= {arXiv preprint arXiv:1304.3918},
year = {2013}
}
Comments
15 pages, 7 figures