通过指数回归模型对帕累托型尾部进行稳健的极值量值估计

IF 0.5 Q4 STATISTICS & PROBABILITY

Communications for Statistical Applications and Methods Pub Date : 2023-11-30 DOI:10.29220/csam.2023.30.6.531

R. Minkah, Tertius de Wet, Abhik Ghosh, H. Yousof

{"title":"通过指数回归模型对帕累托型尾部进行稳健的极值量值估计","authors":"R. Minkah, Tertius de Wet, Abhik Ghosh, H. Yousof","doi":"10.29220/csam.2023.30.6.531","DOIUrl":null,"url":null,"abstract":"The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"23 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust extreme quantile estimation for Pareto-type tails through an exponential regression model\",\"authors\":\"R. Minkah, Tertius de Wet, Abhik Ghosh, H. Yousof\",\"doi\":\"10.29220/csam.2023.30.6.531\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries\",\"PeriodicalId\":44931,\"journal\":{\"name\":\"Communications for Statistical Applications and Methods\",\"volume\":\"23 1\",\"pages\":\"\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications for Statistical Applications and Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29220/csam.2023.30.6.531\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications for Statistical Applications and Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29220/csam.2023.30.6.531","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}

引用次数: 0

摘要

估计极值量值是极值统计（涉及罕见事件的估计）的主要目标之一。本文考虑了重尾分布极值量值的稳健估计方法。该估计器是通过指数回归模型的最小密度功率发散准则获得的。在模拟研究中，将所提出的估计器与文献中的两个极端量值估计器进行了比较。结果表明，与现有的极值量化估计器相比，所提出的估计器对顶阶统计量数量的选择很稳定，偏差和均方误差也较小。我们还利用医药和保险行业的数据说明了所提估计方法的实际应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Robust extreme quantile estimation for Pareto-type tails through an exponential regression model

The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Communications for Statistical Applications and Methods STATISTICS & PROBABILITY-

CiteScore

0.90

自引率

0.00%

发文量

期刊介绍： Communications for Statistical Applications and Methods (Commun. Stat. Appl. Methods, CSAM) is an official journal of the Korean Statistical Society and Korean International Statistical Society. It is an international and Open Access journal dedicated to publishing peer-reviewed, high quality and innovative statistical research. CSAM publishes articles on applied and methodological research in the areas of statistics and probability. It features rapid publication and broad coverage of statistical applications and methods. It welcomes papers on novel applications of statistical methodology in the areas including medicine (pharmaceutical, biotechnology, medical device), business, management, economics, ecology, education, computing, engineering, operational research, biology, sociology and earth science, but papers from other areas are also considered.