Improved Penalty Strategies in Linear Regression Models

Bahadır Yüzbaşı, S. Ejaz Ahmed, Mehmet Güngör
2022
We suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators. Further, a Monte Carlo simulation study is conducted to assess the relative performance of the listed estimators. Also, we numerically compare their performance with Lasso, adaptive Lasso and SCAD strategies. Finally, a real data example is presented to illustrate the usefulness of the suggested methods.
doi:10.57805/revstat.v15i2.212 fatcat:lh2jd26xu5hwzp74qnhgrbyzuu