On Some Ridge Regression Estimators: An Empirical Comparisons

• Published in: Communications in statistics. Simulation and computation Vol. 38; no. 3; pp. 621 - 630
• Main Authors: Muniz, Gisela, Kibria, B. M. Golam
• Format: Journal Article
• Language: English
• Published: Colchester Taylor & Francis Group 01-03-2009; Taylor & Francis
• Subjects: Bias; Estimation; Exact sciences and technology; Linear inference, regression; Mathematics; MSE; Multicollinearity; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Ridge regression; Sciences and techniques of general use; Simulation; Statistics; Ridge regression; Parameter estimation; Simulation. 62J07; Error estimation; Bias; Estimation; Statistical estimation; Regression analysis; Statistical simulation; Mean estimation; Mean square error; Statistical method; Statistical regression; Multicollinearity; Numerical simulation; 62F10; Mean error; Application; MSE
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610910802592838

In ridge regression analysis, the estimation of the ridge parameter k is an important problem. Many methods are available for estimating such a parameter. This article reviewed and proposed some estimators based on Kibria (
2003
) and Khalaf and Shukur (
2005
). A simulation study has been made and mean squared error (MSE) criteria are used to compare the performances of the estimators. We observed that under certain conditions some of the proposed estimators performed well compared to the ordinary least squared (OLS) estimator and some existing popular estimators. Finally, a numerical example has been considered to illustrate the performance of the estimators.