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

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

REVSTAT-Statistical Journal, vol.24, no.1, pp.45-70, 2026 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.57805/revstat.v24i1.605
  • Journal Name: REVSTAT-Statistical Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, MathSciNet, zbMATH, Directory of Open Access Journals
  • Page Numbers: pp.45-70
  • Keywords: biased regression, Liu Estimator, Liu-type estimator, multicollinearity, Ridge Estimator
  • Istanbul University Affiliated: Yes

Abstract

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.