Robust mixture modelling using sub-Gaussian stable distribution

Mahdi Teimouri; Saeid Rezakhah; Adel Mohammdpour

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arXiv · 1701.06749

Robust mixture modelling using sub-Gaussian stable distribution

Mahdi Teimouri·Saeid Rezakhah·Adel Mohammdpour

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Abstract

Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian $α$-stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that estimates parameters of a mixture of sub-Gaussian stable distributions. A comparative study, in the presence of some well-known mixture models, is performed to show the robustness and performance of the mixture of sub-Gaussian $α$-stable distributions for modelling, simulated, synthetic, and real data.

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Mahdi Teimouri, Saeid Rezakhah, Adel Mohammdpour. 2017-01-24. Robust mixture modelling using sub-Gaussian stable distribution. https://arxiv.org/abs/1701.06749

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