Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients

Ridge regression is a widely used method to mitigate the multicollinearly problem often arising in multiple linear regression. It is well known that the ridge regression estimator can be derived from the Bayesian framework by the posterior mode under a multivariate normal prior. However, the ridge regression model with a copula-based multivariate prior model has not been employed in the Bayesian framework. Motivated by the multicollinearly problem due to an interaction term, we adopt a vine copula to construct the copula-based joint prior distribution. For selected copulas and hyperparameters, we propose Bayesian ridge estimators and credible intervals for regression coefficients. A simulation study is carried out to compare the performance of four different priors (the Clayton, Gumbel, and Gaussian copula priors, and the tri-variate normal prior) on the regression coefficients. Our simulation studies demonstrate that the Archimedean (Clayton and Gumbel) copula priors give more accurate estimates in the presence of multicollinearity compared with the other priors. Finally, a real dataset is analyzed, where the Bayesian ridge estimators and some frequentist estimators are compared.