The multivariate Gaussian tail model: an application to oceanographic data
The multivariate Gaussian tail model: an application to oceanographic data
复制标题
多元高斯尾部模型：在海洋学数据中的应用
DOI：
10.1111/1467-9876.00177
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
J. Tawn
中科院分区：
文献类型：
--
作者：
P. Bortot;S. Coles;J. Tawn
海堤的优化设计需要对各种海洋学数据进行极值分析。渐进论点建议使用多元极值模型，但基于英国多个地点数据的实证研究表明，此类模型不足以对此类数据中经常遇到的依赖类型进行建模。本文开发了一个特定的模型的基础上的边际变换的尾部的多元高斯分布，并探讨其效用，克服了目前的方法遇到的局限性。诊断模型的开发和模型的鲁棒性证明通过模拟研究。我们的分析集中在英格兰西南部港口纽林的极端海平面上，以前的研究对洪水的可能性给出了相互矛盾的估计。新的诊断表明，这种差异可能是由于在极端水平之间的弱依赖波周期和波高和静水水位。多元高斯尾模型解决了这一矛盾，并对纽林的极端海况过程提供了令人信服的描述。
Optimal design of sea‐walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies based on data from several UK locations have revealed an inadequacy of this class for modelling the types of dependence that are often encountered in such data. This paper develops a specific model based on the marginal transformation of the tail of a multivariate Gaussian distribution and examines its utility in overcoming the limitations that are encountered with the current methodology. Diagnostics for the model are developed and the robustness of the model is demonstrated through a simulation study. Our analysis focuses on extreme sea‐levels at Newlyn, a port in south‐west England, for which previous studies had given conflicting estimates of the probability of flooding. The novel diagnostics suggest that this discrepancy may be due to the weak dependence at extreme levels between wave periods and both wave heights and still water levels. The multivariate Gaussian tail model is shown to resolve the conflict and to offer a convincing description of the extremal sea‐state process at Newlyn.