Error bounds for asymptotic approximations of the linear discriminant function when the sample sizes and dimensionality are large

Theoretical accuracies are studied For asymtotic approximations of the expe cted probabilities of misclassification (EPMC) when the linear discriminant function is used to classify an observation as coining from one of two mul tivariate normal populations with a common covariance matrix. The asymptoti c approximations considered are the ones under the situation where both the sample sizes and the demensionality are large. We give explicit error boun ds for asymptotic approximations of EPMC, based on a general approximation result. We also discuss with a method of obtaining asymptotic expansions fo r EPMC and their error bounds. (C) 2000 Academic Press.