by J. Wade Davis, Dean M. Young and Karin B. Ernstrom-Keim

Journal of Data Science, v.3, no.4, 449-464

Abstract

We compare two linear dimension-reduction methods for statistical discrimination in terms of average probabilities of misclassification
in reduced dimensions. Using Monte Carlo simulation we compare the dimension-reduction methods over several different parameter
configurations of multivariate normal populations and find that the two methods yield very different results. We also apply the two
dimension-reduction methods examined here to data from a study on football helmet design and neck injuries.

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