Estimation of the covariate conditional tail expectation : a depth-based level set approach

TL;DR

This paper introduces a new depth-based method for estimating the multivariate Covariate-Conditional-Tail-Expectation (CCTE), providing asymptotic analysis, a plug-in estimator, and a case study with Mahalanobis depth.

Contribution

It develops a novel depth-based level set approach for CCTE estimation, including a consistent estimator with convergence rates and practical simulation results.

Findings

Consistent estimator for CCTE with convergence rate

Effective plug-in approach for depth-based level set estimation

Simulation confirms estimator performance

Abstract

The aim of this paper is to study the asymptotic behavior of a particular multivariate risk measure, the Covariate-Conditional-Tail-Expectation (CCTE), based on a multivariate statistical depth function. Depth functions have become increasingly powerful tools in nonparametric inference for multivariate data, as they measure a degree of centrality of a point with respect to a distribution. A multivariate risks scenario is then represented by a depth-based lower level set of the risk factors, meaning that we consider a non-compact setting. More precisely, given a multivariate depth function D associated to a fixed probability measure, we are interested in the lower level set based on D. First, we present a plug-in approach in order to estimate the depth-based level set. In a second part, we provide a consistent estimator of our CCTE for a general depth function with a rate of convergence, and we consider the particular case of the Mahalanobis depth. A simulation study complements the performances of our estimator.