广义帕累托分布形状参数估计的收缩方法

IF 1.2 Q2 MATHEMATICS, APPLIED
Wilhemina Adoma Pels, Atinuke O. Adebanji, Sampson Twumasi-Ankrah, Richard Minkah
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引用次数: 0

摘要

广义帕累托分布是极端统计中最重要的分布之一,在金融、保险、水文等领域有着广泛的应用。本文提出了两种估计广义帕累托分布形状参数的新方法。该方法利用收缩原理对已有的经验贝叶斯方法进行改进,采用基于数据的先验和似然矩方法得到两个估计量。通过仿真研究,将所提估计量与现有的广义Pareto分布形状参数估计量(即极大似然估计量、似然矩估计量等)的性能进行了比较。结果表明,本文提出的估计方法对轻尾分布的形状参数的估计在小到中等的超越次数下表现较好,对重尾分布的估计具有竞争力。用气候和保险研究的实际数据集说明了所提出的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shrinkage Methods for Estimating the Shape Parameter of the Generalized Pareto Distribution
The generalized Pareto distribution is one of the most important distributions in statistics of extremes as it has wide applications in fields such as finance, insurance, and hydrology. This study proposes two new methods for estimating the shape parameter of the generalized Pareto distribution (GPD). The proposed methods use the shrinkage principle to adapt the existing empirical Bayesian with data-based prior and the likelihood moment method to obtain two estimators. The performance of the proposed estimators is compared with the existing estimators (i.e., maximum likelihood, likelihood moment estimators, etc.) for the shape parameter of the generalized Pareto distribution in a simulation study. The results show that the proposed estimators perform better for small to moderate number of exceedances in estimating shape parameter of the light-tailed distributions and competitive when estimating heavy-tailed distributions. The proposed estimators are illustrated with practical datasets from climate and insurance studies.
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来源期刊
Journal of Applied Mathematics
Journal of Applied Mathematics MATHEMATICS, APPLIED-
CiteScore
2.70
自引率
0.00%
发文量
58
审稿时长
3.2 months
期刊介绍: Journal of Applied Mathematics is a refereed journal devoted to the publication of original research papers and review articles in all areas of applied, computational, and industrial mathematics.
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