Multivariate mixture modeling using skew-normal independent distributions

In this paper we consider a flexible class of models, with elements that are finite mixtures of multivariate skew-normal independent distributions. A general EM-type algorithm is employed for iteratively computing parameter estimates and this is discussed with emphasis on finite mixtures of skew-normal, skew-t, skew-slash and skew-contaminated normal distributions. Further, a general information-based method for approximating the asymptotic covariance matrix of the estimates is also presented. The accuracy of the associated estimates and the efficiency of some information criteria are evaluated via simulation studies. Results obtained from the analysis of artificial and real data sets are reported illustrating the usefulness of the proposed methodology. The proposed EM-type algorithm and methods are implemented in the R package mixsmsn.

► We consider a finite mixture of multivariate SNI distributions. ► A general EM-type algorithm is proposed for computing parameter estimates. ► An information-based method for approximating the covariance matrix is presented. ► The proposed methods are implemented in the R package mixsmsn().