Kibria, B.M.G. (2003) Performance of Some New Ridge Regression Estimators. Communications in Statistics—Theory and Methods, 32, 419-435. https://doi.org/10.1081/SAC-120017499

has been cited by the following article:

• TITLE: Improving the Ordinary Least Squares Estimator by Ridge Regression

  AUTHORS: Ghadban Khalaf

  KEYWORDS: OLS Estimator, Multicollinearity, Ridge Regression, Simulation

  JOURNAL NAME: Open Access Library Journal, Vol.9 No.5, May 27, 2022

  ABSTRACT: In the presence of multicollinearity, ridge regression techniques result in estimated coefficients that are biased but have smaller variance than Ordinary Least Squares estimators and may, therefore, have a smaller Mean Squares Error (MSE). The ridge solution is to supplement the data by stochastically shrinking the estimates toward zero. In this study, we propose a new estimator to reduce the effect of multicollinearity and improve the estimation. We show by a simulation study that the MSE of the suggested estimator is lower than other estimators of the ridge and the OLS estimators.