Procedure for the Selection of Principal Components in Principal Components Regression
주성분회귀분석에서 주성분선정을 위한 새로운 방법

Bu-Yong Kim, Myung-Hee Shin
2010 Korean Journal of Applied Statistics  
Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are
more » ... rger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.
doi:10.5351/kjas.2010.23.5.967 fatcat:5hdtgyuasfegvpoedgpus34g24