Component selection norms for principal components regression

Abstract

Abstract Multicollinearity or near exact linear dependence among the vectors of regressor variables in a multiple linear regression analysis can have important effects on the quality of least squares parameter estimates. One frequently suggested approach for these problems is principal components regression. This paper investigates alternative variable selection procedures and their implications for such an analysis. Keywords: regression analysismulticollinearityleast squarespredictionnormsprincipal components