论文标题
扩展广义帕累托分布的分布回归模型
Distributional regression models for Extended Generalized Pareto distributions
论文作者
论文摘要
扩展的广义帕累托分布（EGPD）（Naveau等人，2016年）是一个分布家族，已引入该家族，用于对一个正随机变量的全部范围进行建模，但根据阈值峰值峰值方法分配了下部和上尾。本文的目的是扩大EGPD的应用范围，从而使分析师能够纳入协变量对模型的影响。特别是我们介绍了一个规范，其中可以将EGPD的参数建模为协变量的加法功能，例如空间或时间。作为一种相关产品，我们提供了用R编写的附加代码，该代码足够灵活，可以以通用的方式实现EGPD，从而允许引入新的参数表单。我们展示了在法国西北地区的小时降雨建模上的潜力，并讨论了建模策略。
The Extended Generalized Pareto Distribution (EGPD) (Naveau et al. 2016) is a family of distribution that has been introduced to model the full range of a positive random variable but with the lower and the upper tails distributed according to the peaks-over-threshold methodology. The aim of this article is to augment the scope of application of EGPD allowing the analyst to incorporate the effect of covariates on the model. In particular we introduce a specification where the parameters of EGPD can be modeled as additive functions of the covariates, e.g. space or time. As a related product we provide an add-on code written in R that it is flexible enough to implement the EGPD in a generic way, allowing to introduce new parametric forms. We show the potential of our add-on on the modeling of hourly rainfalls over the North-West region of France and discuss modeling strategies.
