The EMMIX software for the fitting of mixtures of normal and t-components

Abstract

We consider the fitting of normal mixture models to multivariate data, using maximum likelihood via the EM algorithm. This approach requires the initial specification of an initial estimate of the vector of unknown parameters, or equivalently, of an initial classification of the data with respect to the components of the mixture model under fit. We describe an algorithm called EMMIX that automatically undertakes this fitting, including the provision of suitable initial values if not supplied by the user. The EMMIX algorithm has several options, including the option to carry out a resampling-based test for the number of components in the mixture model. 1 INTRODUCTION Finite mixtures models are being increasingly used to model the distributions of a wide variety of random phenomena (McLachlan, 1998). For multivariate data of a continuous nature, attention has focussed on the use of multivariate normal components because of their computational convenience. They can be easily fitted iter...