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Extreme value analysis of actuarial risks: estimation and model validation

机译:精算风险的极值分析:估计和模型验证

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摘要

We give an overview of several aspects arising in the statistical analysis of extreme risks with actuarial applications in view. In particular it is demonstrated that empirical process theory is a very powerful tool, both for the asymptotic analysis of extreme value estimators and to devise tools for the validation of the underlying model assumptions. While the focus of the paper is on univariate tail risk analysis, the basic ideas of the analysis of the extremal dependence between different risks are also outlined. Here we emphasize some of the limitations of classical multivariate extreme value theory and sketch how a different model proposed by Ledford and Tawn can help to avoid pitfalls. Finally, these theoretical results are used to analyze a data set of large claim sizes from health insurance.
机译:鉴于精算应用,我们对极端风险的统计分析中出现的几个方面进行了概述。特别是,它证明了经验过程理论是一种非常强大的工具,既可以用于极值估计量的渐近分析,也可以设计用于验证基础模型假设的工具。尽管本文的重点是单变量尾部风险分析,但也概述了分析不同风险之间的极端依赖性的基本思想。在这里,我们强调了经典多元极值理论的一些局限性,并概述了Ledford和Tawn提出的不同模型如何帮助避免陷阱。最后,这些理论结果用于分析来自健康保险的大额索赔数据。

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  • 来源
    《AStA Advances in statistical analysis》 |2012年第2期|p.225-264|共40页
  • 作者

    Holger Drees;

  • 作者单位

    Department of Mathematics, SPST, University of Hamburg, Bundesstr. 55, 20146 Hamburg,Germany;

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  • 正文语种 eng
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  • 入库时间 2022-08-18 02:30:24

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