{"title":"Topics in data analysis using R in extreme value theory","authors":"Helena Penalva, M. Neves, Sandra Nunes","doi":"10.51936/qsdg2096","DOIUrl":null,"url":null,"abstract":"The statistical Extreme Value Theory has grown gradually from the beginning of the 20th century. Its unquestionable importance in applications was definitely recognized after Gumbel's book in 1958, Statistics of Extremes. Nowadays there is a wide number of applied sciences where extreme value statistics are largely used. So, accurately modeling extreme events has become more and more important and the analysis requires tools that must be simple to use but also should consider complex statistical models in order to produce valid inferences. To deal with accurate, friendly, free and open-source software is of great value for practitioners and researchers. This paper presents a review of the main steps for initializing a data analysis of extreme values in R environment. Some well documented packages are briefly described and two data sets will be considered for illustrating the use of some functions.","PeriodicalId":242585,"journal":{"name":"Advances in Methodology and Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Methodology and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51936/qsdg2096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
The statistical Extreme Value Theory has grown gradually from the beginning of the 20th century. Its unquestionable importance in applications was definitely recognized after Gumbel's book in 1958, Statistics of Extremes. Nowadays there is a wide number of applied sciences where extreme value statistics are largely used. So, accurately modeling extreme events has become more and more important and the analysis requires tools that must be simple to use but also should consider complex statistical models in order to produce valid inferences. To deal with accurate, friendly, free and open-source software is of great value for practitioners and researchers. This paper presents a review of the main steps for initializing a data analysis of extreme values in R environment. Some well documented packages are briefly described and two data sets will be considered for illustrating the use of some functions.
统计极值理论从20世纪初开始逐渐发展起来。在甘贝尔1958年的著作《极端统计》(Statistics of Extremes)之后,它在应用中的重要性得到了肯定。目前,在许多应用科学中都大量使用了极值统计。因此,准确地模拟极端事件变得越来越重要,分析需要的工具必须简单易用,但也应该考虑复杂的统计模型,以便产生有效的推论。处理准确、友好、免费和开源的软件对从业者和研究人员具有重要的价值。本文介绍了在R环境中初始化极值数据分析的主要步骤。简要描述一些文档完备的包,并考虑使用两个数据集来说明一些函数的使用。