Simulation Study for Penalized Bayesian Elastic Net Quantile Regression

Bayesian regression analysis has great importance in recent years, especially the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing prior distribution of interested parameter is main idea analysis. By penalizing model, variance estimators are reduced notable and bias getting smaller. The tradeoff between penalized estimator consequently produce more interpretable model with prediction accuracy. In this paper, we proposed new hierarchical for quantile by employing scale mixture normals mixing truncated gamma that stated (Li Lin, 2010) Laplace distribution. Therefore, Gibbs sampling algorithms introduced. A comparison made classical lasso conducting simulations studies. Our comparable gives better results.