On the Pickands stochastic process

TL;DR

This paper develops a stochastic process framework for the asymptotic behavior of the Pickands process, generalizing classical estimates and establishing weak convergence to a Gaussian process using advanced empirical process techniques.

Contribution

It introduces a new stochastic process perspective for the Pickands process and proves its weak convergence to a Gaussian process, simplifying previous results.

Findings

Uniform convergence of the process margins to the extremal index

Weak convergence of the Pickands process to a Gaussian process

Application of weighted empirical process approximation techniques

Abstract

We consider the Pickands process {equation*} P_{n}(s)=\log (1/s)^{-1}\log \frac{X_{n-k+1,n}-X_{n-[k/s]+1,n}}{% X_{n-[k/s]+1,n}-X_{n-[k/s^{2}]+1,n}}, {equation*} {equation*} (\frac{k}{n}\leq s^2 \leq 1), {equation*} which is a generalization of the classical Pickands estimate $P_{n}(1/2)$ of the extremal index. We undertake here a purely stochastic process view for the asymptotic theory of that process by using the Cs\"{o}rg\H{o}-Cs\"{o}rg\H{o}-Horv\'{a}th-Mason (1986) \cite{cchm} weighted approximation of the empirical and quantile processes to suitable Brownian bridges. This leads to the uniform convergence of the margins of this process to the extremal index and a complete theory of weak convergence of $P_n$ in $\ell^{\infty}([a,b])$ to some Gaussian process $$\{\mathbb{G},a\leq s \leq b\} $$ for all $[a,b] \subset]0,1[$. This frame greatly simplifies the former results and enable applications based on stochastic processes methods.