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

Journal
Matematichki Bilten
Date Issued
2017-01-01
Author(s)
Dimovski, Marko
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.
Subjects

linear regression, re...

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