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Finite mixture of skewed sub-Gaussian stable distributions

arXiv 2205.14067 ↗First posted 27 May 2022stat.ME cs.LG stat.MLcited by 0

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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.

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

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References · 7

  1. 2013A Mixture of Generalized Hyperbolic Distributionscited by 18
  2. 2015Mixtures of Multivariate Power Exponential Distributionscited by 6
  3. 2013Extending mixtures of factor models using the restricted multivariate skew-normal distributioncited by 3
  4. 2017Flexible Clustering for High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Modelscited by 2
  5. 2013Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Datacited by 1
  6. 2013A Mixture of Generalized Hyperbolic Factor Analyzerscited by 1
  7. 2014Finite Mixtures of Canonical Fundamental Skew t-Distributionscited by 1

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