A latent trawl process model for extreme values
A latent trawl process model for extreme values
复制标题
极值潜拖网过程模型
DOI：
10.21314/jem.2018.179
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Gandy
中科院分区：
文献类型：
--
作者：
Ragnhild C. Noven;Almut E. D. Veraart;A. Gandy
本文提出了一种新的模型，用于表征阈值以上的时间依赖性。该模型基于拖网过程类，拖网过程是平稳的、无限可分的随机过程。极值模型是通过在一个分层框架中嵌入一个拖网过程来构建的，这确保了边际分布是广义帕累托分布，正如经典极值理论所期望的那样。我们还考虑了一个修改后的版本，这个模型的工作范围更广的广义帕累托分布类，并具有分离边际和时间依赖属性的优势。该模型的应用程序说明环境时间序列，它表明，该模型提供了相当大的灵活性，在捕捉极端值数据的依赖结构。
This paper presents a new model for characterising temporal dependence in exceedances above a threshold. The model is based on the class of trawl processes, which are stationary, infinitely divisible stochastic processes. The model for extreme values is constructed by embedding a trawl process in a hierarchical framework, which ensures that the marginal distribution is generalised Pareto, as expected from classical extreme value theory. We also consider a modified version of this model that works with a wider class of generalised Pareto distributions, and has the advantage of separating marginal and temporal dependence properties. The model is illustrated by applications to environmental time series, and it is shown that the model offers considerable flexibility in capturing the dependence structure of extreme value data.