• Corpus ID: 218516559

A Bayesian approach for clustering skewed data using mixtures of multivariate normal-inverse Gaussian distributions

@article{Fang2020ABA,
  title={A Bayesian approach for clustering skewed data using mixtures of multivariate normal-inverse Gaussian distributions},
  author={Yuan Fang and Dimitris Karlis and Sanjeena Subedi},
  journal={arXiv: Computation},
  year={2020}
}
Non-Gaussian mixture models are gaining increasing attention for mixture model-based clustering particularly when dealing with data that exhibit features such as skewness and heavy tails. Here, such a mixture distribution is presented, based on the multivariate normal inverse Gaussian (MNIG) distribution. For parameter estimation of the mixture, a Bayesian approach via Gibbs sampler is used; for this, a novel approach to simulate univariate generalized inverse Gaussian random variables and… 
1 Citations

Figures and Tables from this paper

A Bayesian Approach for Partial Gaussian Graphical Models With Sparsity
TLDR
This work explores various Bayesian approaches to estimate partial Gaussian graphical models and reformulated an existing result for model selection consistency to stick to sparse and group-sparse settings, providing a theoretical guarantee under some technical assumptions.

References

SHOWING 1-10 OF 60 REFERENCES
Clustering with the multivariate normal inverse Gaussian distribution
Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions
TLDR
Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian distributions is achieved through variational Bayes approximations through a substantial departure from the traditional EM approach.
Mixtures of Shifted Asymmetric Laplace Distributions
A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the
Mixtures of Shifted AsymmetricLaplace Distributions
TLDR
This work marks an important step in the non-Gaussian model-based clustering and classification direction, and a variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the generalized inverse Gaussian distribution.
Robust mixture modeling using multivariate skew t distributions
This paper presents a robust mixture modeling framework using the multivariate skew t distributions, an extension of the multivariate Student’s t family with additional shape parameters to regulate
Finite mixture modelling using the skew normal distribution
Normal mixture models provide the most popular framework for mod- elling heterogeneity in a population with continuous outcomes arising in a variety of subclasses. In the last two decades, the skew
Model-based clustering with non-elliptically contoured distributions
TLDR
Finite mixtures of the normal inverse Gaussian distribution (and its multivariate extensions) are proposed, which start from a density that allows for skewness and fat tails, generalize the existing models, are tractable and have desirable properties.
Robust mixture modeling using the skew t distribution
TLDR
This article proposes a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings and presents analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates.
A mixture of generalized hyperbolic distributions
TLDR
The ability of the authors' models to recover parameters for data from underlying Gaussian and skew‐t distributions is demonstrated and the role of generalized hyperbolic mixtures within the wider model‐based clustering, classification, and density estimation literature is discussed.
...
...