Parameter estimation of the generalized Pareto distribution—Part II |
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| Authors: | P. de Zea Bermudez Samuel Kotz |
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| Affiliation: | 1. Departamento de Estatística e Investigação Operaciona, Faculdade de Ciências da Universidade de Lisboa, Bloco C6, Piso 4, Campo Grande, 1749-016 Lisboa, Portugal and CEAUL;2. School of Engineering and Applied Science, Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC, USA |
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| Abstract: | 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 GPD 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. |
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| Keywords: | Generalized Pareto distribution Order statistics Robust methods Bayesian inference |
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