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

Elisabeth Armaut (1) , Roland Diel (1) , Thomas Laloë (2, 1)

1 LJAD - Laboratoire Jean Alexandre Dieudonné
2 JAD - Laboratoire Jean Alexandre Dieudonné

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 probability measure P on R d and a depth function D(•, P), we are interested in the α-lower level set L D (α) := z ∈ R d : D(z, P) ≤ α. First, we present a plug-in approach in order to estimate L D (α). 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.

Mots clés: Plug-in estimation; multivariate depth function; Mahalanobis depth; risk theory

Domaines: Mathématiques [math]; Statistiques [stat]

Elisabeth Armaut, Roland Diel, Thomas Laloë. Estimation of the covariate conditional tail expectation : a depth-based level set approach. 2021. ⟨hal-03336080v1⟩