  Performance accuracy of the Euclidean Distance Discriminant rule (EDDR) is
studied in the high-dimensional asymptotic framework which allows the
dimensionality to exceed sample size. Under mild assumptions on the traces of
the covariance matrix, our new results provide the asymptotic distribution of
the conditional misclassification error and the explicit expression for the
consistent and asymptotically unbiased estimator of the expected
misclassification error. To get these properties, new results on the asymptotic
normality of the quadratic forms and traces of the higher power of Wishart
matrix, are established. Using our asymptotic results, we further develop two
generic methods of determining a cut-off point for EDDR to adjust the
misclassification errors. Finally, we numerically justify the high accuracy of
our asymptotic findings along with the cut-off determination methods in finite
sample applications, inclusive of the large sample and high-dimensional
scenarios.
