A class of unbiased location invariant Hill-type estimators for heavy tailed distributions

被引：12
|
作者：
Li, Jiaona [1 ]
Peng, Zuoxiang [1 ]
Nadarajah, Saralees [2 ]
机构：
[1] Southwest Univ, Sch Math & Stat, Chongqing 400715, Peoples R China
[2] Univ Manchester, Sch Math, Manchester, Lancs, England
来源：
ELECTRONIC JOURNAL OF STATISTICS | 2008年 / 2卷
关键词：
Asymptotic normality; Location invariant Hill-type heavy tailed index estimator; Second order regular variation;
D O I：
10.1214/08-EJS276
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：
Based on the methods provided in Caeiro and Comes (2002) and Fraga Alves (2001), a new class of location invariant Hill-type estimators is derived in this paper. Its asymptotic distributional representation and asymptotic normality are presented, and the optimal choice of sample fraction by Mean Squared Error is also discussed for some special cases. Finally comparison studies are provided for some familiar models by Monte Carlo simulations.
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页码：829 / 847
页数：19
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