The efficiency of a linear discriminant function based on unclassified initial samples

Abstract

Estimation of the optimal linear discriminant function is considered on the basis of a sample of observations known only to belong to a mixture of two univariate normal populations with a common variance. The asymptotic efficiency of the procedure so obtained is evaluated relative to that of Anderson's classification statistic.