A New General Class of Ridge-type Estimator in Linear Regression Models

AKAY K. U., ERTAN E., Erkoç A., TAŞ F.

REVSTAT-Statistical Journal, cilt.24, sa.1, ss.45-70, 2026 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.57805/revstat.v24i1.605
  • Dergi Adı: REVSTAT-Statistical Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, MathSciNet, zbMATH, Directory of Open Access Journals
  • Sayfa Sayıları: ss.45-70
  • Anahtar Kelimeler: biased regression, Liu Estimator, Liu-type estimator, multicollinearity, Ridge Estimator
  • İstanbul Üniversitesi Adresli: Evet

Özet

In linear regression models, researchers have developed new biased estimators to mitigate the effects of multicollinearity instead of using the Ordinary Least Squares (OLS) estimator, which is affected by multicollinearity. In this study, we define a general class of estimators called Ridge-type estimators (RTE). The superiority of RTE over other biased estimators is investigated under the matrix mean square error criterion. In addition, two separate Monte Carlo simulation studies are conducted to compare the performance of the considered biased estimators. A numerical example is given to demonstrate the superiority of the proposed estimator over other biased estimators.