Robustness of Fisher's Linear Discriminant Function Under Two-Component Mixed Normal Models
研究了当两个总体分布为已知参数的两成分混合正态分布时，Fisher线性判别函数的稳健性，发现当分布偏离正态不大且距离适中时，该函数较为稳健。
Abstract Robustness of Fisher's linear discriminant function is evaluated when the distributions of the two populations are characterized by two-component mixed normal distributions with known parameters. The results suggest that the linear discriminant function is rather robust when the two distributions do not markedly deviate from normality and are moderately distant, particularly if they are similar in shape.