Simulation Study to Verify the Appropriate k Value for Ridge Regression in Two-variable Regression Model

Hanan Duzan  and Nurul Sima Binti Mohamad Shariff nbsp

doi:10.17485/ijst/2015/v8i12/70649

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Simulation Study to Verify the Appropriate k Value for Ridge Regression in Two-variable Regression Model

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2015/v8i12/70649

Year: 2015, Volume: 8, Issue: 12, Pages: 1-8

Original Article

Simulation Study to Verify the Appropriate k Value for Ridge Regression in Two-variable Regression Model

Hanan Duzan^* and Nurul Sima Binti Mohamad Shariff

Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Malaysia; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study investigates the problem of using Ordinarily Least Squares (OLS) estimators in the presence of multicollinearity in regression analysis. As an alternative of OLS is ridge regression, which it is believed to be superior to least-squares regression in the presence of multicollinearity. The robustness of this method is investigated and comparison is made with the least squares method via simulation studies. Our results have shown that the system stabilizes in a region of k, which k is a positive quantity less than one and whose values depend on the degree of correlation between the independent variables. The results illustrate that k is a non-linear function of the correlation between the independent variables (r12).

Keywords: Least Squares Method, Linear Models, Multicollinearity, Ridge Regression