文章基本信息

标题：A Bayesian approach to extended models for exceedance
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作者：Fernando Ferraz do Nascimento ; Marcelo Bourguignon Pereira
期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2017
卷号：31
期号：4
页码：801-820
DOI：10.1214/17-BJPS378
语种：English
出版社：Brazilian Statistical Association
摘要：In extreme value theory, the generalized Pareto distribution (GPD) is a family of continuous distribution used to model the tail of the distribution to values higher than a threshold $u$. Several works have used this method to approximate the tail of distribution. In this paper, we propose two extensions of GPD, including an additional shape parameter, to provide a more flexible distribution for exceedance. Some properties of these approximations are presented. Inference for these extensions were performed under the Bayesian paradigm, and the results showed fit improvement when compared with the standard GPD in applications to environmental and financial data.