文章基本信息

标题：A Bayesian semi-parametric approach to extreme regime identification
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作者：Fernando Ferraz do Nascimento ; Dani Gamerman ; Richard Davis 等
期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2016
卷号：30
期号：4
页码：540-561
DOI：10.1214/15-BJPS293
语种：English
出版社：Brazilian Statistical Association
摘要：The limiting tail behaviour of distributions is known to follow one of three possible limiting distributions, depending on the domain of attraction of the observational model under suitable regularity conditions. This work proposes a new approach for identification and analysis of the shape parameter of the GPD as a mixture distribution over the three possible regimes. This estimation is based on evaluation of posterior probabilities for each regime. The model-based approach uses a mixture at the observational level where a Generalized Pareto distribution (GPD) is assumed above the threshold, and mixture of Gammas distributions is used under a threshold. The threshold is also estimated. Simulation exercises were conducted to evaluate the accuracy of the model for various parameter settings and sample sizes, specifically in the estimation of high quantiles. They show an improved performance over existing approaches. The paper also compares inferences based on Bayesian regime choice against Bayesian averaging over the regimes. Results of environmental applications show the correctly identifying the GPD regime plays a vital role in these studies.