Alternative Approaches to Missing Values in Discriminant Analysis

Abstract This paper compares by simulations several methods of handling missing observations in discrimination. In an earlier paper, several methods were compared in discriminating by the usual linear discriminant function between two multivariate normal populations in which all pairs of variables are equally correlated. In the present study, a variety of population matrices was used and two additional methods were introduced: the first, a simpler regression technique and the second, a modified technique based on the first principal component. The new regression technique was found to give relatively high probabilities of correct classification.