A method for comparing the cross validated classification accuracies of linear and quadratic classification rules is presented under varying data conditions for the k-group classification problem. With this method, separate-group as well as total-group proportions of correct classifications can be compared for the two rules. McNemar's test for…

Formulas for estimating cross-validated hit-rates, the number of correct classifications into an a priori grouping structure, were examined. The following mathematical formulas were compared: McLachlan's formula estimator, two Snappin and Knoke smoothed formula estimators, and the analytic leave-one-out estimator. The R method was included as a…

An empirical method (PRESS) for examining and contrasting the cross-validated prediction accuracies of some popular algorithms for weighting predictor variables was advanced and examined. The sample specific PRESS method was suggested as most appropriate when selecting among several alternative weighting algorithms to achieve maximum…

An empirical method called Predicted Error Sum of Squares (PRESS) is advanced and studied. This method is used to examine the cross-validated prediction accuracies of some popular algorithms for weighted predictor variables. The weighting methods that were considered were ordinary least squares, ridge regression, regression on principal…