title: Parsimonious mixtures of multivariate contaminated normal distributions.
abstract: A mixture of multivariate contaminated normal distributions is developed for model-based clustering. In addition to the parameters of the classical normal mixture, our contaminated mixture has, for each cluster, a parameter controlling the proportion of mild outliers and one specifying the degree of contamination. Crucially, these parameters do not have to be specified a priori, adding a flexibility to our approach. Parsimony is introduced via eigen-decomposition of the component covariance matrices, and sufficient conditions for the identifiability of all the members of the resulting family are provided. An expectation-conditional maximization algorithm is outlined for parameter estimation and various implementation issues are discussed. Using a large-scale simulation study, the behavior of the proposed approach is investigated and comparison with well-established finite mixtures is provided. The performance of this novel family of models is also illustrated on artificial and real data.
authors: Department of Economics and Business, University of Catania, Catania, Italy. antonio.punzo@unict.it.
authors: Antonio
authors: A
authors: Punzo
authors: Department of Mathematics and Statistics, McMaster University, Hamilton, Canada.
authors: Paul D
authors: PD
authors: McNicholas
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015233:Models, Statistical; D016011:Normal Distribution
keywords: Contaminated normal distribution; Contamination; EM algorithm; Mixture models; Model-based clustering