EVALUATING ALTERNATIVE LINEAR PROGRAMMING MODELS TO SOLVE THE TWO‐GROUP DISCRIMINANT PROBLEM
研究了三类线性规划模型在两组判别问题中的表现，实验涵盖正态与非正态总体，发现最小化超出两组边界偏差之和的模型是传统线性判别技术的有力替代。
ABSTRACT The two‐group discriminant problem has applications in many areas, for example, differentiating between good credit risks and poor ones, between promising new firms and those likely to fail, or between patients with strong prospects for recovery and those highly at risk. To expand our tools for dealing with such problems, we propose a class of nonpara‐metric discriminant procedures based on linear programming (LP). Although these procedures have attracted considerable attention recently, only a limited number of computational studies have examined the relative merits of alternative formulations. In this paper we provide a detailed study of three contrasting formulations for the two‐group problem. The experimental design provides a variety of test conditions involving both normal and nonnormal populations. Our results establish the LP model which seeks to minimize the sum of deviations beyond the two‐group boundary as a promising alternative to more conventional linear discriminant techniques.