A central limit theorem for functions of stationary max-stable random fields on $\mathbb{R}^d$

TL;DR

This paper establishes a central limit theorem for functions of stationary max-stable random fields on Euclidean space, with applications to risk assessment of natural disasters.

Contribution

It introduces a general CLT for functions of max-stable fields on  and demonstrates its validity for specific models like Brown-Resnick and Smith fields.

Findings

Valid CLT for functions of max-stable fields on 

Application to risk assessment in natural disasters

Extension of CLT theory to spatial extreme models

Abstract

Max-stable random fields are very appropriate for the statistical modelling of spatial extremes. Hence, integrals of functions of max-stable random fields over a given region can play a key role in the assessment of the risk of natural disasters, meaning that it is relevant to improve our understanding of their probabilistic behaviour. For this purpose, in this paper, we propose a general central limit theorem for functions of stationary max-stable random fields on $\mathbb{R}^d$. Then, we show that appropriate functions of the Brown-Resnick random field with a power variogram and of the Smith random field satisfy the central limit theorem. Another strong motivation for our work lies in the fact that central limit theorems for random fields on $\mathbb{R}^d$ have been barely considered in the literature. As an application, we briefly show the usefulness of our results in a risk assessment context.