Continuum Regression and Ridge Regression

We demonstrate the close relationship between first-factor continuum regression and standard ridge regression. The difference is that continuum regression inserts a scalar compensation factor for that part of the shrinkage in ridge regression that has no connection with tendencies towards collinearity. We interpret this to mean that first-factor continuum regression is preferable in principle to ridge regression if we want protection against near colliearity but do not admit shrinkage as a general principle. Furthermore, our experience indicates that with first-factor continuum regression we can obtain predictors that are at least as mean-squared error efficient as with ridge regression but with less sensitivity to the choice of ridge constant