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Finite mixtures of multivariate skew t-distributions: some recent and new results
Finite mixtures of multivariate skew t (MST) distributions have proven to be useful in modelling heterogeneous data with asymmetric and heavy tail behaviour. Recently, they have been exploited as anExpand
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On mixtures of skew normal and skew $$t$$-distributions
Finite mixtures of skew distributions have emerged as an effective tool in modelling heterogeneous data with asymmetric features. With various proposals appearing rapidly in the recent years, whichExpand
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Model-based clustering and classification with non-normal mixture distributions
Non-normal mixture distributions have received increasing attention in recent years. Finite mixtures of multivariate skew-symmetric distributions, in particular, the skew normal and skewExpand
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EMMIXuskew: An R Package for Fitting Mixtures of Multivariate Skew t Distributions via the EM Algorithm
This paper describes an algorithm for fitting finite mixtures of unrestricted Multivariate Skew t (FM-uMST) distributions. The package EMMIXuskew implements a closed-form expectation-maximizationExpand
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On the fitting of mixtures of multivariate skew t-distributions via the EM algorithm
We show how the expectation-maximization (EM) algorithm can be applied exactly for the fitting of mixtures of general multivariate skew t (MST) distributions, eliminating the need for computationallyExpand
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Finite mixtures of canonical fundamental skew t-distributions - The unification of the restricted and unrestricted skew t-mixture models
This paper introduces a finite mixture of canonical fundamental skew t (CFUST) distributions for a model-based approach to clustering where the clusters are asymmetric and possibly long-tailed (in:Expand
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Finite mixtures of canonical fundamental skew $$t$$t-distributions
This paper introduces a finite mixture of canonical fundamental skew $$t$$t (CFUST) distributions for a model-based approach to clustering where the clusters are asymmetric and possibly long-tailedExpand
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The skew-t factor analysis model
Factor analysis is a classical data reduction technique that seeks a potentially lower number of unobserved variables that can account for the correlations among the observed variables. This paperExpand
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Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of aExpand
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A robust factor analysis model using the restricted skew-$$t$$t distribution
Factor analysis is a classical data-reduction technique that seeks a potentially lower number of unobserved variables that can account for the correlations among the observed variables. This paperExpand
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