  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 $\alpha$-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 $\alpha$-stable distributions for modelling, simulated, synthetic,
and real data.
