Finite mixture of skewed sub-Gaussian stable distributions
This is an incremental improvement for robust clustering in statistics and data science.
The authors tackled the problem of robust model-based clustering by proposing a finite mixture of skewed sub-Gaussian stable distributions, which outperforms normal and skewed normal mixtures due to heavier tails, as demonstrated on simulation and real data.
We propose the finite mixture of skewed sub-Gaussian stable distributions. The maximum likelihood estimator for the parameters of proposed finite mixture model is computed through the expectation-maximization algorithm. The proposed model contains the finite mixture of normal and skewed normal distributions. Since the tails of proposed model is heavier than even the Student's t distribution, it can be used as a powerful model for robust model-based clustering. Performance of the proposed model is demonstrated by clustering simulation data and two sets of real data.