Parameter estimation of the generalized Pareto distribution-Part II

被引:73
作者
Bermudez, P. de Zea [1 ]
Kotz, Samuel [2 ]
机构
[1] Univ Lisbon, Fac Ciencias, Dept Estatist & Invest Operac, P-1749016 Lisbon, Portugal
[2] George Washington Univ, Sch Engn & Appl Sci, Dept Engn Management & Syst Engn, Washington, DC 20052 USA
关键词
Generalized Pareto distribution; Order statistics; Robust methods; Bayesian inference; REDUCING VARIANCE; REGRESSION; KERNEL; SPLINES; ERROR;
D O I
10.1016/j.jspi.2008.11.020
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
This is the second part of a paper which focuses on reviewing methods for estimating the parameters of the generalized Pareto distribution (GPD). The GPD is a very important distribution in the extreme value context. It is commonly used for modeling the observations that exceed very high thresholds. The ultimate success of the GPD in applications evidently depends on the parameter estimation process. Quite a few methods exist in the literature for estimating the GPD parameters. Estimation procedures, such as the maximum likelihood (ML), the method of moments (MOM) and the probability weighted moments (PWM) method were described in Part I of the paper. We shall continue to review methods for estimating the GIRD parameters. in particular methods that are robust and procedures that use the Bayesian methodology. As in Part I, we shall focus on those that: are relatively simple and straightforward to be applied to real world data. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:1374 / 1397
页数:24
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