Discrimination with jointly equicorrelated multi-level multivariate data

Abstract

In this article we study a linear as well as a quadratic discriminant function for multi-level multivariate repeated measurement data under the assumption of multivariate normality. We assume that the m-variate observations have jointly equicorrelated covariance structure in addition to a Kronecker product structure on the mean vector. The new discriminant functions are very effective in discriminating individuals when the number of observations is very small. The proposed classification rules are demonstrated on a real data set. The error rates of the proposed classification rules are found to be much less than the error rates of the traditional classification rules, when in fact the traditional classification rules fail most of the time owing to the small sample sizes.