Robust Trimmed Likelihood Discriminant Analysis for Multivariate Repeated Data

Abstract

Repeated measures discriminant analysis (RMDA) have been developed for distinguishing between two or more independent groups in multivariate repeated measures designs, in which multiple outcomes are repeatedly measured at two or more measurement occasions. However, these models, which are based on structured covariances, rely on the assumption of multivariate normality. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on maximum likelihood estimators and robust maximum trimmed likelihood estimators under a variety of data analytic conditions. RMDA based on robust estimators are recommended for discriminating between population in multivariate repeated measures designs characterized by non-normal distributions.