We propose a new method for modeling the distribution function of high dimensional extreme value distributions. The Pickands dependence function models the relationship between the covariates in the tails, and we learn this function using a neural network that is designed to satisfy its required properties. Moreover, we present new methods for recovering the spectral representation of extreme distributions and propose a generative model for sampling from extreme copulas. Numerical examples are provided demonstrating the efficacy and promise of our proposed methods.

翻译：我们提出了一个新的方法来模拟高维极端值分布分布的分布功能。Pickands依赖功能模型来模拟尾部的共变体之间的关系,我们用一个旨在满足其必要特性的神经网络来学习这一功能。此外,我们提出了恢复极端分布的光谱分布的新方法,并提出了一个从极端相交体采样的基因模型。提供了数字例子来说明我们拟议方法的有效性和前景。