Improved Penalty Strategies in Linear Regression Models

Authors

  • Bahadır Yüzbaşı Inonu University
  • S. Ejaz Ahmed Brock University
  • Mehmet Güngör Inonu University

DOI:

https://doi.org/10.57805/revstat.v15i2.212

Keywords:

sub-model, Full Model, Pretest and Shrinkage Estimation, Multicollinearity, Asymptotic and Simulation

Abstract

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.

Published

2017-04-18

How to Cite

Yüzbaşı , B., Ejaz Ahmed , S., & Güngör , M. (2017). Improved Penalty Strategies in Linear Regression Models. REVSTAT-Statistical Journal, 15(2), 251–276. https://doi.org/10.57805/revstat.v15i2.212