. IMPROVED PENALTY STRATEGIES in LINEAR REGRESSION MODELS

YÜZBAŞI B., Ahmed S. E., GÜNGÖR M.

REVSTAT-STATISTICAL JOURNAL, vol.15, no.2, pp.251-276, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 2
  • Publication Date: 2017
  • Journal Name: REVSTAT-STATISTICAL JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.251-276
  • Keywords: Sub-model, Full Model, Pretest and Shrinkage Estimation, Multicollinearity, Asymp-totic and Simulation, ORACLE PROPERTIES, SHRINKAGE, LIKELIHOOD, ESTIMATORS, SELECTION, LASSO
  • Inonu University Affiliated: Yes

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.