<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes,Dora Susana Raposo Prata</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A log probability weighted moment estimator of extreme quantiles</style></title><secondary-title><style face="normal" font="default" size="100%">Theory and Practice of Risk Assessment - ICRA5 2013</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Extreme quantile</style></keyword><keyword><style  face="normal" font="default" size="100%">extreme value index</style></keyword><keyword><style  face="normal" font="default" size="100%">Log probability weighted moment</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal level</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84946404285&amp;partnerID=8YFLogxK</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York LLC</style></publisher><volume><style face="normal" font="default" size="100%">136</style></volume><pages><style face="normal" font="default" size="100%">293 - 303</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we consider the semi-parametric estimation of extreme quantiles of a right heavy-tail model. We propose a new Probability Weighted Moment estimator for extreme quantiles, which is obtained from the estimators of the shape and scale parameters of the tail. Under a second-order regular variation condition on the tail, of the underlying distribution function, we deduce the non degenerate asymptotic behaviour of the estimators under study and present an asymptotic comparison at their optimal levels. In addition, the performance of the estimators is illustrated through an application to real data.In this paper we consider the semi-parametric estimation of extreme quantiles of a right heavy-tail model. We propose a new Probability Weighted Moment estimator for extreme quantiles, which is obtained from the estimators of the shape and scale parameters of the tail. Under a second-order regular variation condition on the tail, of the underlying distribution function, we deduce the non degenerate asymptotic behaviour of the estimators under study and present an asymptotic comparison at their optimal levels. In addition, the performance of the estimators is illustrated through an application to real data.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;sem pdf conforme despacho&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1007/978-3-319-18029-8_22</style></custom2></record></records></xml>