Bias correction in multivariate extremes

TL;DR

This paper develops bias correction methods for estimating multivariate extremal dependence, introducing new asymptotically unbiased estimators that simplify threshold selection and improve estimation accuracy.

Contribution

It extends bias correction techniques from univariate to multivariate extremes, proposing new estimators with regular behavior and practical aggregation methods.

Findings

New asymptotically unbiased estimators for multivariate tail dependence.

Simplified threshold selection through aggregated estimators.

Validated performance via simulation and real data application.

Abstract

The estimation of the extremal dependence structure is spoiled by the impact of the bias, which increases with the number of observations used for the estimation. Already known in the univariate setting, the bias correction procedure is studied in this paper under the multivariate framework. New families of estimators of the stable tail dependence function are obtained. They are asymptotically unbiased versions of the empirical estimator introduced by Huang [Statistics of bivariate extremes (1992) Erasmus Univ.]. Since the new estimators have a regular behavior with respect to the number of observations, it is possible to deduce aggregated versions so that the choice of the threshold is substantially simplified. An extensive simulation study is provided as well as an application on real data.