A comparison of asymptotic error rate expansions for the sample linear discriminant function

摘要

Several asymptotic expansions for approximating the expected or unconditional probability of misclassification for the sample linear discriminant function are compared for accuracy in terms of yielding the smallest mean absolute deviation from the exact value for 104 population configurations. The actual expected probabilities of misclassification are found via Monte Carlo simulation. A simple and relatively obscure asymptotic expansion derived by Raudys (Tech. Cybern.4, 168–174, 1972) is found to yield better approximation than the well-known asymptotic expansions.