A0777
Title: On factor copula-based mixed regression models
Authors: Bouchra Nasri - University of Montreal (Canada) [presenting]
Bruno Remillard - HEC Montreal (Canada)
Pavel Krupskiy - Melbourne University (Australia)
Abstract: A copula-based method for mixed regression models is proposed, where the conditional distribution of the response variable, given covariates, is modeled by a parametric family of continuous or discrete distributions, and a latent variable models the dependence between observations in each cluster. The estimation of copula and margin parameters is demonstrated, outlining the procedure for determining the asymptotic behavior of the estimation errors. Numerical experiments are performed to assess the precision of the estimators for finite samples. An example of its application is given using COVID-19 vaccination hesitancy data from several countries. All developed methodologies are implemented in CopulaGAMM, available in CRAN.
