Simulation Study for Variable Selection in Quantile Regression

Six essential techniques for variable selection in quantile regression models are thoroughly examined in this work. Quantile Lasso, gamma-divergence, quantile elastic net, quantile adaptive Lasso, quantile SCAD, and quantile MCP are some of these techniques. The study's main objective is to compare and assess various approaches' performance, particularly in relation to their capacity to reduce median model error. An important part of the analysis includes situations in which there are more possible candidate variables than there are data. Through a thorough simulation analysis, the study also aims to identify the best practices for each and every linear regression situation.