The bivariate $K$-finite normal mixture "blanket" copula: an application to driving patterns

There are many bivariate parametric copulas in the literature to model bivariate data with different dependence features. We propose a new bivariate parametric copula family that cannot only handle various dependence patterns that appear in the existing parametric bivariate copula families, but also provides a more enriched dependence structure. The proposed copula construction exploits finite mixtures of bivariate normal distributions. The mixing operation, the distinct correlation and mean parameters at each mixture component introduce quite a flexible dependence. We apply the new copula to real transportation data that cannot apparently be modelled by any of the existing parametric families of bivariate copulas.