一种基于贝叶斯的广义Pareto分布变点估计  

A Bayesian Based Generalized Pareto Distribution Change Point Estimation

作　　者：王国琴 吴有富 许婷 欧永玲 WANG Guo-qin;WU You-fu;XU Ting;OU Yong-ling(School of Data Science and Information Engineering,Guizhou Minzu University;Guizhou Communication Vocational College,Guiyang 550025,China)

机构地区：[1]贵州民族大学数据科学与信息工程学院,贵州贵阳550025 [2]贵州交通职业技术学院,贵州贵阳550025

出　　处：《遵义师范学院学报》2024年第1期108-112,共5页Journal of Zunyi Normal University

摘　　要：文章研究广义Pareto分布单变点的估计问题,利用贝叶斯方法对广义Pareto分布变点进行估计,模拟结果表明,贝叶斯方法能获得更好的效果。同时将贝叶斯方法与基于KL散度似然比统计量的极大似然法比较,得出贝叶斯方法效果更好。The article studies the estimation problem of the single change point of the generalized Pareto distribution,and uses Bayesian methods to estimate the change point of the generalized Pareto distribution.Simulation results show that Bayesian methods can achieve better results.At the same time,comparing the Bayesian method with the maximum likelihood method based on KL divergence likelihood ratio statistic,it is found that the Bayesian method has better performance.

关 键 词：广义PARETO分布 贝叶斯估计 极大似然估计 

分 类 号：O212.8[理学—概率论与数理统计]