<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</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, M.Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-parametric tail inference through probability-weighted moments.</style></title><secondary-title><style face="normal" font="default" size="100%">J. Stat. Plann. Inference</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">extreme value index</style></keyword><keyword><style  face="normal" font="default" size="100%">first order scale parameter</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">statistics of extremes}</style></keyword><keyword><style  face="normal" font="default" size="100%">{heavy tails</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">141</style></volume><pages><style face="normal" font="default" size="100%">937-950</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: For heavy-tailed models, and working with the sample of the $k$ largest observations, we present probability weighted moments (PWM) estimators for the first order tail parameters. Under regular variation conditions on the right-tail of the underlying distribution function $F$ we prove the consistency and asymptotic normality of these estimators. Their performance, for finite sample sizes, is illustrated through a small-scale Monte Carlo simulation.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
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