Classification into two Populations for Time Dependent Observations

Abstract

Optimal classification rules based on linear functions which maximize the area under the relative operating characteristic curve or which maximize the chosen probabilistic distance between two populations are studied here. We obtain an expression for the optimal linear discriminant function and show that the resulting procedure belongs to the Anderson-Bahadur admissible class. The asymptotic form of the discriminant function is also studied.