论文标题
用于阈值超过峰的非平稳性极值分析的惩罚分段线性模型
A penalised piecewise-linear model for non-stationary extreme value analysis of peaks over threshold
论文作者
论文摘要
metocean极端通常会随着方向和季节等协变量而系统变化。在这项工作中，我们提出了非平稳模型，以相对于一维协变量，峰值变量阈值的峰值的大小和速率。使用协变量的模型参数的变化使用在协变量域上定义的一个或两个维度定义的一个或两个维度中的分段线性函数来描述。参数粗糙度受到调节，以提供最佳的预测性能，并在惩罚的推理框架内使用交叉验证评估。使用Bootstrap重新采样来量化参数不确定性。这些模型用于估计风暴峰值的极端，相对于北海北部的一个地点的方向和季节，波峰峰值显着。基于六个节点的方向季节域的三角剖分的协变量表示可提供良好的预测性能。惩罚的分段线性框架以合理的计算成本提供了协变量效应的灵活表示。
Metocean extremes often vary systematically with covariates such as direction and season. In this work, we present non-stationary models for the size and rate of occurrence of peaks over threshold of metocean variables with respect to one- or two-dimensional covariates. The variation of model parameters with covariate is described using a piecewise-linear function in one or two dimensions defined with respect to pre-specified node locations on the covariate domain. Parameter roughness is regulated to provide optimal predictive performance, assessed using cross-validation, within a penalised likelihood framework for inference. Parameter uncertainty is quantified using bootstrap resampling. The models are used to estimate extremes of storm peak significant wave height with respect to direction and season for a site in the northern North Sea. A covariate representation based on a triangulation of the direction-season domain with six nodes gives good predictive performance. The penalised piecewise-linear framework provides a flexible representation of covariate effects at reasonable computational cost.
