2009•Unpublished venueRequires access
Principal Components Regression Methods
Yazid M. Al-Hassan, Mowafaq Muhammed Al-Kassab
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Abstract
A basic assumption concerned with general linear regression model is that there is no correlation (or no multicollinearity) between the explanatory variables. When this assumption is not satisfied, the least squares estimators have large variances and become unstable and may have a wrong sign. Therefore, we resort to biased regression methods, which stabilize the parameter estimates. Ridge regression (RR) and principal components regression (PCR) are two of the most popular biased regression methods. In this article, we used Monte Carlo experiments to estimate the regression coefficients by RR and PCR methods. A comparison between RR and PCR methods was made in the sense of having smaller mean squares error (MSE). Based on this simulation study, we found that RR method performs better than PCR method. Mathematics Subject Classification: 62J07; 62H25; 65C05