Regularized least-square optimization method for variable selection in regression models

Abstract

A new type of regularization in least-square optimization for variable selection in regression models is proposed. Proposed regularization is suitable for regression models with equal or at least comparable regressors’ influence. Consistency of the estimator of the regression parameter under suitable assumptions is shown. Numerical results demonstrate efficiency of the proposed regularization and its better performance compared to existing regularization methods.