Variable screening for covariate dependent extreme value index estimation

TL;DR

This paper introduces a new covariate screening method for estimating the covariate-dependent extreme value index, improving variable selection in extreme value analysis with demonstrated effectiveness through simulations and real data.

Contribution

It proposes a sure independence screening technique using the conditional Pickands estimator for covariate selection in extreme value index estimation.

Findings

Method performs well in finite samples

Effective variable selection demonstrated in simulations

Applicable to real-world data analysis

Abstract

One of the main topics of extreme value analysis is to estimate the extreme value index, an important parameter that controls the tail behavior of the distribution. In many cases, estimating the extreme value index of the target variable associated with covariates is useful. Although the estimation of the covariate-dependent extreme value index has been developed by numerous researchers, no results have been presented regarding covariate selection. This paper proposes a sure independence screening method for covariate-dependent extreme value index estimation. For the screening, the marginal utility between the target variable and each covariate is calculated using the conditional Pickands estimator. A single-index model that uses the covariates selected by screening is further provided to estimate the extreme value index after screening. Monte Carlo simulations confirmed the finite sample performance of the proposed method. In addition, a real-data application is presented.