Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

YÜZBAŞI B., Arashi M., Ahmed S. E.

INTERNATIONAL STATISTICAL REVIEW, cilt.88, sa.1, ss.229-251, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 88 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1111/insr.12351
  • Dergi Adı: INTERNATIONAL STATISTICAL REVIEW
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, INSPEC, zbMATH, DIALNET
  • Sayfa Sayıları: ss.229-251
  • Anahtar Kelimeler: Generalised ridge regression, low-dimensional and high-dimensional data, multicollinearity, penalty estimation, shrinkage estimation, VARIABLE SELECTION, LIKELIHOOD, LASSO
  • İnönü Üniversitesi Adresli: Evet

Özet

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.