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逻辑回归相对于线性判别分析的渐近效率AI 解读

We calculate the asymptotic efficiency of logistic regression relative to linear discriminant analysis for testing hypotheses about the parameters when the explanatory variables are normally distributed. The Pitman asymptotic efficiency for hypothesis tests in this context is the same as the asymptotic relative efficiency calculated using mis-classification rates and is independent of the number of explanatory variables and the number of parameters to be tested.

逻辑回归相对于线性判别分析的渐近效率AI 解读

SUMMARY We calculate the asymptotic efficiency of logistic regression relative to linear discriminant analysis for testing hypotheses about the parameters when the explanatory variables are normally distributed. The Pitman asymptotic efficiency for hypothesis tests in this context is the same as the asymptotic relative efficiency calculated using misclassification rates and is independent, of the number of explanatory variables and the number of parameters to be tested.