A Bayesian approach for clustering skewed data using mixtures of multivariate normal-inverse Gaussian distributions

Abstract

Non-Gaussian mixture models are gaining increasing attention for mixture model-based clustering particularly when dealing with data that exhibit features such as skewness and heavy tails. Here, such a mixture distribution is presented, based on the multivariate normal inverse Gaussian (MNIG) distribution. For parameter estimation of the mixture, a Bayesian approach via Gibbs sampler is used; for this, a novel approach to simulate univariate generalized inverse Gaussian random variables and matrix generalized inverse Gaussian random matrices is provided. The proposed algorithm will be applied to both simulated and real data. Through simulation studies and real data analysis, we show parameter recovery and that our approach provides competitive clustering results compared to other clustering approaches.

arXiv comment40 pages, 7 figures

References in corpus (5)

• A Mixture of Generalized Hyperbolic Distributions
• Mixtures of Shifted Asymmetric Laplace Distributions
• Mixtures of Skew-t Factor Analyzers
• Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions
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Cited by in corpus (1)

• Gibbs Sampler for Matrix Generalized Inverse Gaussian Distributions