ERROR RATES OF NONBAYES CLASSIFICATION RULES AND THE ROBUSTNESS OF FISHER LINEAR DISCRIMINANT FUNCTION
成果类型：
Article
署名作者：
ONEILL, TJ
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.2307/2337158
发表日期：
1992
页码：
177184
关键词：
摘要：
We call a classification procedure non-Bayes if it does not converge to the Bayes classification procedure. An asymptotic expansion is found for the expected error rate of such a classification rule. This is used to compare the estimates of Fisher's linear discriminant rule, F, and the quadratic discriminant rule, Q, under departures from the equal variance matrices assumption. It is found that F is quite robust to departures from the equal variances assumption.