Estimation of Error Rates in Several-Population Discriminant Analysis

Abstract

Researchers seeking to estimate the classification accuracy of linear discriminant functions in a more than two-population setting have had little guidance as to the most appropriate technique. The authors review the available techniques and present an additional alternative which combines features of the U-method and the recently developed posterior probability estimator. The new alternative is compared with other methods by Monté Carlo simulation.