Robust multivariate classification procedures based on the mml estimators

We develop a classification procedure based on Tiku's (1967, 1982) MML (modified maximum likelihood) estimators of location and scale parameters for classifying an observation in one of the two p-variate normal populations II1 and II2. We investigate the effects of non-normality on the errors of mis-classification e. and e1 and e2 and show that this new procedure is superior to the classical procedure (based on sample means and variances) and …