Discrimination with Mixed Binary and Continuous Data
本文通过案例研究比较了Fisher线性判别函数、逻辑判别和核方法等判别分析技术在混合二元与连续数据上的表现，使用留一法评估误分类率。
SUMMARY This paper consists of a case study in the use of different methods of Discriminant analysis. Methods used include Fisher's Linear discriminant function, various modifications of this technique, Logistic discrimination and Kernel methods using jack-knife maximum likelihood. These methods are applied on two sets of data. Their success is compared using the proportions misclassified, estimated by the leaving-one-out method where possible.