Speaker
Description
Recently, the penalized regularization regression methods have been extensively studied in the literature, but it is still difficult to generalize a method to be applied in various applications, particularly that suffer from heterogeneity and collinearity. Due to that, we compare the developed machine learning methods using numerical simulations for various senario. The applied methods in this research are elastic-net, and adaptive lasso where the weights are based on a quantile regression estimator as remedies at τ = 0.25, 0.5, 0.75, besides that we applied the adaptive lasso weighted based on the Ridge regression. Therefore, the methods' performance are verified by the criteria RSS, RMSE, MAE, MAPE, and MASE. Overall, the finding of the application shows that the quantile elastic-net regression generally outperforms all other methods with respect to all measures.
