Back to overview

Exceedance-based nonlinear regression of tail dependence

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author MhallaLinda, OptizThomas, Chavez-DemoulinValérie,
Project Predicting patient flow congestion using extreme value theory
Show all

Original article (peer-reviewed)

Journal Extremes
Volume (Issue) 22
Page(s) 523 - 552
Title of proceedings Extremes

Open Access

Type of Open Access Publisher (Gold Open Access)


The probability and structure of co-occurrences of extreme values in multivariate data may critically depend on auxiliary information provided by covariates. In this contribution, we develop a flexible generalized additive modeling framework based on high threshold exceedances for estimating covariate-dependent joint tail characteristics for regimes of asymptotic dependence and asymptotic independence. The framework is based on suitably defined marginal pretransformations and projections of the random vector along the directions of the unit simplex, which lead to convenient univariate representations of multivariate exceedances based on the exponential distribution. Good performance of our estimators of a nonparametrically designed influence of covariates on extremal coefficients and tail dependence coefficients are shown through a simulation study.