Second-order asymptotic expansion for the risk in classification of curved exponential populations

Abstract

This paper deals with the problem of classifying an observation into one of two curved exponential populations. The plug-in classification rule obtained by replacing unknown parameters with their bias-adjusted ML estimators in Bayes classification rule is used. The second-order asymptotic expansion with respect to the inverses of the training sample sizes for the expected regret risk is derived.