Abstract
Finite mixtures of multivariate skew distributions have become increasingly popular in recent years due to their flexibility and robustness in modeling heterogeneity, asymmetry and leptokurticness of the data. This paper introduces a novel finite mixture of multivariate scale-shape mixtures of skew-normal distributions to enhance strength and flexibility when modeling heterogeneous multivariate data that contain more extreme non-normal features. A computational tractable ECM algorithm which consists of analytically simple E- and CM-steps is developed to carry out maximum likelihood estimation of parameters. The asymptotic covariance matrix of parameter estimates is derived from the observed information matrix using the outer product of expected complete-data scores. We demonstrate the utility of the proposed approach through simulated and real data examples.
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