Semi-parametric tail inference through probability-weighted moments

被引：20
|
作者：
Caeiro, Frederico [1 ,2 ]
Gomes, M. Ivette [3 ,4 ]
机构：
[1] Univ Nova Lisboa, P-2829516 Caparica, Portugal
[2] Fac Ciencias & Tecnol, CMA, P-2829516 Caparica, Portugal
[3] Univ Lisbon, DEIO, P-1749016 Lisbon, Portugal
[4] Fac Ciencias Lisboa, CEAUL, P-1749016 Lisbon, Portugal
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2011年 / 141卷 / 02期
关键词：
Heavy tails; Extreme value index; First order scale parameter; Semi-parametric estimation; Statistics of extremes; ASYMPTOTIC NORMALITY; INDEX ESTIMATION; BIAS; ESTIMATORS; PARAMETERS; HILL;
D O I：
10.1016/j.jspi.2010.08.015
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, 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. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码：937 / 950
页数：14
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