A comparison of three linear rules for classification (original) (raw)

Applied Stochastic Models and Data Analysis, 1986

Abstract

This paper compares three different linear procedures for classification: the normal one, the canonical one and a distribution‐free one recently described by Heuchenne. The study is mainly conducted using a simulation which makes it possible to compute the probabilities of correct allocation of the three methods in 3888 different cases. The normal rule looks slightly better than Heuchenne's, which looks clearly better than the canonical one. Finally, inference on Heuchenne's method is examined and conditions under which this method is optimal are given.

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