On the Investigation of Alternative Regressions by Principal Component Analysis

In a multiple regression problem, let the p × 1 vector x consist of the dependent variable and p – 1 predictor variables. The correlation matrix of x is reduced to principal components. The components corresponding to low eigenvalues may be useful in suggesting possible alternative subregressions. This possibility is analysed, and formulae derived for the derivation of subregressions from the principal components.