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Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions. (English) Zbl 1459.62122

Summary: Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are considered. The use of variational Bayes approximations here is a substantial departure from the traditional EM approach and alleviates some of the associated computational complexities and uncertainties. Our variational algorithm is applied to simulated and real data. The paper concludes with discussion and suggestions for future work.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)

Software:

R; robustbase; rrcov; MASS (R)
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References:

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