A mixture of generalized hyperbolic distributions
By Ryan P. Browne, Paul David McNicholas. 2013
216 citations recorded by the source corpus.
We introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives within which the mixture of multivariate t‐distributions and the mixture of skew‐t distributions predominate. The mathematical development of our mixture of generalized hyperbolic distributions model relies on its relationship with the generalized inverse Gaussian distribution. The latter is reviewed before our mixture models are presented along with details of the aforesaid reliance. Parameter estimation is outlined within the expectation–maximization framework before the clustering performance of our mixture models is illustrated via applications on simulated and real data. In particular, the ability of our models to recover parameters for data from underlying Gaussian and skew‐t distributions is demonstrated. Finally, the role of generalized hyperbolic mixtures within the wider model‐based clustering, classification, and density estimation literature is discussed. The Canadian Journal of Statistics 43: 176–198; 2015 © 2015 Statistical Society of Canada
Subfields: Artificial Intelligence, Epidemiology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b7532a0de1b6012e82199105c2d789dc78332d6e
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In Artificial Intelligence
In Epidemiology
In Statistics and Probability
Citations in the corpus
- Model-Based Clustering (2016, background): “…2011; Vrbik and McNicholas 2012, 2014; Murray, McNicholas, and Browne 2014a), shifted asymmetric Laplacemixtures (e.g., Franczak, Browne, andMcNicholas 2014), variance-gammamixtures (McNicholas, McNicholas, and Browne 2014), and generalized hyperbolic mixtures (Browne and McNicholas 2015).”
- Explaining individual predictions when features are dependent: More accurate approximations to Shapley values (2019, unknown): “Provided that v D is pre-computed, all that is needed to explain different predictions from the same model is to perform the matrix multiplication of R D and v D for the different v D”