AN EXPERIMENTAL COMPARISON OF STATISTICAL AND LINEAR PROGRAMMING APPROACHES TO THE DISCRIMINANT PROBLEM
实验比较了三种线性规划方法和Fisher判别法在判别问题上的表现，基于36种实验条件下的测试结果，提出了根据Hotelling T²和Box M统计量选择方法的样本规则。
ABSTRACT This paper reports the results of an experimental comparison of three linear programming approaches and the Fisher procedure for the discriminant problem. The linear programming approaches include two formulations proposed by Freed and Glover and a newly proposed mixed‐integer, linear goal programming formulation. Ten test problems were generated for each of the 36 cells in the three‐factor, full‐factorial experimental design. Each test problem consisted of a 30‐case estimation sample and a 1,000‐case holdout sample. Experimental results indicate that each of the four approaches was statistically preferable in certain cells of the experimental design. Sample‐based rules are suggested for selecting an approach based on Hotelling's T 2 and Box's M statistics. Subject Areas: Statistical Techniques, Linear Statistical Models, and Linear Programming.