. IMPROVED PENALTY STRATEGIES in LINEAR REGRESSION MODELS

YÜZBAŞI B., Ahmed S. E., GÜNGÖR M.

REVSTAT-STATISTICAL JOURNAL, cilt.15, sa.2, ss.251-276, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 2
  • Basım Tarihi: 2017
  • Dergi Adı: REVSTAT-STATISTICAL JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.251-276
  • Anahtar Kelimeler: Sub-model, Full Model, Pretest and Shrinkage Estimation, Multicollinearity, Asymp-totic and Simulation, ORACLE PROPERTIES, SHRINKAGE, LIKELIHOOD, ESTIMATORS, SELECTION, LASSO
  • İnönü Üniversitesi Adresli: Evet

Özet

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.