Robust mixture modelling using the t distribution
Statistics and Computing
Enhanced Model-Based Clustering, Density Estimation,and Discriminant Analysis Software: MCLUST
Journal of Classification
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Robust mixture modeling using the skew t distribution
Statistics and Computing
Multivariate mixture modeling using skew-normal independent distributions
Computational Statistics & Data Analysis
Clustering and classification via cluster-weighted factor analyzers
Advances in Data Analysis and Classification
Using evolutionary algorithms for model-based clustering
Pattern Recognition Letters
On mixtures of skew normal and skew $$t$$-distributions
Advances in Data Analysis and Classification
Dimension reduction for model-based clustering via mixtures of multivariate $$t$$t-distributions
Advances in Data Analysis and Classification
Using conditional independence for parsimonious model-based Gaussian clustering
Statistics and Computing
A multivariate linear regression analysis using finite mixtures of t distributions
Computational Statistics & Data Analysis
Parsimonious skew mixture models for model-based clustering and classification
Computational Statistics & Data Analysis
Multivariate measurement error models using finite mixtures of skew-Student t distributions
Journal of Multivariate Analysis
Finite mixtures of multivariate skew t-distributions: some recent and new results
Statistics and Computing
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The majority of the existing literature on model-based clustering deals with symmetric components. In some cases, especially when dealing with skewed subpopulations, the estimate of the number of groups can be misleading; if symmetric components are assumed we need more than one component to describe an asymmetric group. Existing mixture models, based on multivariate normal distributions and multivariate t distributions, try to fit symmetric distributions, i.e. they fit symmetric clusters. In the present paper, we propose the use of finite mixtures of the normal inverse Gaussian distribution (and its multivariate extensions). Such finite mixture models start from a density that allows for skewness and fat tails, generalize the existing models, are tractable and have desirable properties. We examine both the univariate case, to gain insight, and the multivariate case, which is more useful in real applications. EM type algorithms are described for fitting the models. Real data examples are used to demonstrate the potential of the new model in comparison with existing ones.