A New Quantile-Based Approach for LASSO Estimation

Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased available literature, including ridge, Least Angle Shrinkage Selection Operator (LASSO), and elastic net. In this work, we study performance classical LASSO, adaptive ordinary least squares (OLS) methods high-multicollinearity scenarios propose some new estimators for estimating LASSO “k”. The proposed is evaluated using extensive Monte Carlo simulations real-life examples. Based on mean square error criterion, results suggest that outperformed existing estimators.