# Finite mixture of skewed sub-Gaussian stable distributions

arXiv: https://arxiv.org/abs/2205.14067 · HTML: https://mlsanity.com/paper/2205.14067/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 7 of 7)

- [A Mixture of Generalized Hyperbolic Distributions](https://mlsanity.com/paper/1305.1036/) (2013)
- [Mixtures of Multivariate Power Exponential Distributions](https://mlsanity.com/paper/1506.04137/) (2015)
- [Extending mixtures of factor models using the restricted multivariate skew-normal distribution](https://mlsanity.com/paper/1307.1748/) (2013)
- [Flexible Clustering for High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Models](https://mlsanity.com/paper/1705.03130/) (2017)
- [Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data](https://mlsanity.com/paper/1305.7344/) (2013)
- [A Mixture of Generalized Hyperbolic Factor Analyzers](https://mlsanity.com/paper/1311.6530/) (2013)
- [Finite Mixtures of Canonical Fundamental Skew t-Distributions](https://mlsanity.com/paper/1405.0685/) (2014)

## Abstract (arXiv)

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.