Copula based learning for directed acyclic graphs

We provide the learning of a DAG model arising from high dimensional random variables following both normal and non-normal assumptions. To this end, the copula function utilized connecting dependent variables. Moreover to normal copula, the three most applicable copulas have been investigated modeling all three dependence structures negative, positive, and weak kinds. The copula functions, FGM, Clayton, and Gumbel are considered coving these situations and their detailed calculations are also presented. In addition, the structure function has been exactly determined due to choosing a good copula model based on statistical software R with respect to any assumed direction among all nodes. The direction with the maximum structure function has been preferred. The corresponding algorithms finding these directions and the maximization procedures are also provided. Finally, some extensive tabulations and simulation studies are provided, and in the following to have a clear thought of provided strategies, a real world application has been analyzed.