Small-sample one-sided testing in extreme value regression models
Statistics Theory
2014-05-26 v1 Methodology
Statistics Theory
Abstract
We derive adjusted signed likelihood ratio statistics for a general class of extreme value regression models. The adjustments reduce the error in the standard normal approximation to the distribution of the signed likelihood ratio statistic. We use Monte Carlo simulations to compare the finite-sample performance of the different tests. Our simulations suggest that the signed likelihood ratio test tends to be liberal when the sample size is not large, and that the adjustments are effective in shrinking the size distortion. Two real data applications are presented and discussed.
Cite
@article{arxiv.1405.6057,
title = {Small-sample one-sided testing in extreme value regression models},
author = {Silvia L. P. Ferrari and Eliane C. Pinheiro},
journal= {arXiv preprint arXiv:1405.6057},
year = {2014}
}
Comments
20 pages and 3 figures