Tail inference using extreme U-statistics

TL;DR

This paper introduces a new class of extreme U-statistics that interpolate between block maxima and peaks-over-threshold methods, establishing their asymptotic properties and proposing a novel estimator for the extreme value index.

Contribution

It develops the theory of extreme U-statistics with location-scale invariant kernels and introduces an extreme Pickands U-estimator with competitive finite-sample performance.

Findings

Asymptotic normality of extreme U-statistics is proven.

A new extreme Pickands U-estimator is proposed.

Finite-sample performance is comparable to pseudo-maximum likelihood methods.

Abstract

Extreme U-statistics arise when the kernel of a U-statistic has a high degree but depends only on its arguments through a small number of top order statistics. As the kernel degree of the U-statistic grows to infinity with the sample size, estimators built out of such statistics form an intermediate family in between those constructed in the block maxima and peaks-over-threshold frameworks in extreme value analysis. The asymptotic normality of extreme U-statistics based on location-scale invariant kernels is established. Although the asymptotic variance coincides with the one of the H\'ajek projection, the proof goes beyond considering the first term in Hoeffding's variance decomposition. We propose a kernel depending on the three highest order statistics leading to a location-scale invariant estimator of the extreme value index resembling the Pickands estimator. This extreme Pickands U-estimator is asymptotically normal and its finite-sample performance is competitive with that of the pseudo-maximum likelihood estimator.