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arxiv: 1604.04793 · v1 · pith:HGM4V4ZDnew · submitted 2016-04-16 · 📊 stat.ME

A double-indexed functional Hill process and applications

classification 📊 stat.ME
keywords equationextremewhoseasymptoticbeginclassestimatorsfunctional
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Let $X_{1,n} \leq .... \leq X_{n,n}$ be the order statistics associated with a sample $X_{1}, ...., X_{n}$ whose pertaining distribution function (% \textit{df}) is $F$. We are concerned with the functional asymptotic behaviour of the sequence of stochastic processes \begin{equation} T_{n}(f,s)=\sum_{j=1}^{j=k}f(j)\left(\log X_{n-j+1,n}-\log X_{n-j,n}\right)^{s}, \label{fme} \end{equation} indexed by some classes $\mathcal{F}$ of functions $f:\mathbb{N}% ^{\ast}\longmapsto \mathbb{R}_{+}$ and $s \in ]0,+\infty[$ and where $k=k(n)$ satisfies \begin{equation*} 1\leq k\leq n,k/n\rightarrow 0\text{as}n\rightarrow \infty . \end{equation*} \noindent We show that this is a stochastic process whose margins generate estimators of the extreme value index when $F$ is in the extreme domain of attraction. We focus in this paper on its finite-dimension asymptotic law and provide a class of new estimators of the extreme value index whose performances are compared to analogous ones. The results are next particularized for one explicit class $\mathcal{F}$.

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