Asymptotic efficiency of logistic regression relative to linear discriminant analysis

Abstract

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.