The General Distribution of the Error Rate of a Classification Procedure with Application to Logistic Regression Discrimination
研究了任意分类规则估计量的错误率的大样本分布，并给出了逻辑回归估计量的渐近分布，发现非正态情况下逻辑回归分类效率较低，建议尽可能使用最大似然判别。
Abstract The large-sample distribution of the error rate of an arbitrary estimator of the optimal classification rule is given. The asymptotic distribution of the logistic regression estimator is found. These results are used to show that the efficiency of logistic regression classification in some nonnormal cases is low. This suggests that maximum likelihood discrimination should be used whenever possible.