A new class of copula regression models for modelling multivariate heavy-tailed data
Abstract
A new class of copulas, termed the MGL copula class, is introduced. The new copula originates from extracting the dependence function of the multivariate generalized log-Moyal-gamma distribution whose marginals follow the univariate generalized log-Moyal-gamma (GLMGA) distribution as introduced in \citet{li2019jan}. The MGL copula can capture nonelliptical, exchangeable, and asymmetric dependencies among marginal coordinates and provides a simple formulation for regression applications. We discuss the probabilistic characteristics of MGL copula and obtain the corresponding extreme-value copula, named the MGL-EV copula. While the survival MGL copula can be also regarded as a special case of the MGB2 copula from \citet{yang2011generalized}, we show that the proposed model is effective in regression modelling of dependence structures. Next to a simulation study, we propose two applications illustrating the usefulness of the proposed model. This method is also implemented in a user-friendly R package: \texttt{rMGLReg}.
Cite
@article{arxiv.2108.05511,
title = {A new class of copula regression models for modelling multivariate heavy-tailed data},
author = {Zhengxiao Li and Jan Beirlant and Liang Yang},
journal= {arXiv preprint arXiv:2108.05511},
year = {2021}
}