Bayesian sensitivity analysis for asymmetric copulas with cubic sections

The use of copulas to model the dependence between indicators leads us to observe the different methods of estimation and its applicability, given practical circumstances, such as having small databases. For this reason, Bayesian methods under the scope of copulas come to show immense utility. In this paper, we investigate how the responses of the Asymmetric Cubic Sections copula model are affected when we vary the prior distributions display over the model parameters. We use as a reference setting a non-informative prior distribution and we observe the effect of the other prior distributions in relation to it. We used this diversity of scenarios to map the possible degrees of dependence between two educational scores obtained by students of the undergraduate course of statistics at the University of Campinas in 2014.