A comparison between two distance-based discriminant principles

Abstract

A distance-based classification procedure suggested by Matusita (1956) has long been available as an alternative to the usual Bayes decision rule. Unsatisfactory features of both approaches when applied to multinomial data led Goldstein and Dillon (1978) to propose a new distance-based principle for classification. We subject the Goldstein/Dillon principle to some theoretical scrutiny by deriving the population classification rules appropriate not only to multinomial data but also to multivariate normal and mixed multinomial/multinormal data. These rules demonstrate equivalence of the Goldstein/Dillon and Matusita approaches for the first two data types, and similar equivalence is conjectured (but not explicitly obtained) for the mixed data case. Implications for sample-based rules are noted.