Robustness of the linear discriminant function to nonnormality: Edgeworth series distribution

Abstract The effects of applying the normal classificatory rule to a nonnormal population are studied here. These are assessed through the distribution of the misclassification errors in the case of the Edgeworth type distribution. Both theoretical and empirical results are presented. An examination of the latter shows that the effects of this type of nonnormality are marginal. The probability of misclassification of an observation from ∏ 1 , using the appropriate LR rule, is always larger than one using the normal approximation ( μ 1 μ 2 ). Converse condition holds for the misclassification of an observation from ∏ 2 . Overall error rates are not affected by the skewness factor to any great extent.