The two-group cross-validation classification accuracies of six algorithms (i.e., least squares, ridge regression, principal components, a common factor method, equal weighting, and logistic regression) were compared as a function of degree of validity concentration, group separation, and number of subjects. Therein, the findings of two previous studies were extended to the latter three methods...

Regularized regression methods for linear regression have been developed the last few decades to overcome the flaws of ordinary least squares regression with regard to prediction accuracy. In this chapter, three of these methods (Ridge regression, the Lasso, and the Elastic Net) are incorporated into CATREG, an optimal scaling method for both linear and nonlinear transformation of variables in ...