1,508 research outputs found
Mixtures of Skew-t Factor Analyzers
In this paper, we introduce a mixture of skew-t factor analyzers as well as a
family of mixture models based thereon. The mixture of skew-t distributions
model that we use arises as a limiting case of the mixture of generalized
hyperbolic distributions. Like their Gaussian and t-distribution analogues, our
mixture of skew-t factor analyzers are very well-suited to the model-based
clustering of high-dimensional data. Imposing constraints on components of the
decomposed covariance parameter results in the development of eight flexible
models. The alternating expectation-conditional maximization algorithm is used
for model parameter estimation and the Bayesian information criterion is used
for model selection. The models are applied to both real and simulated data,
giving superior clustering results compared to a well-established family of
Gaussian mixture models
Parsimonious Shifted Asymmetric Laplace Mixtures
A family of parsimonious shifted asymmetric Laplace mixture models is
introduced. We extend the mixture of factor analyzers model to the shifted
asymmetric Laplace distribution. Imposing constraints on the constitute parts
of the resulting decomposed component scale matrices leads to a family of
parsimonious models. An explicit two-stage parameter estimation procedure is
described, and the Bayesian information criterion and the integrated completed
likelihood are compared for model selection. This novel family of models is
applied to real data, where it is compared to its Gaussian analogue within
clustering and classification paradigms
Copula-type Estimators for Flexible Multivariate Density Modeling using Mixtures
Copulas are popular as models for multivariate dependence because they allow
the marginal densities and the joint dependence to be modeled separately.
However, they usually require that the transformation from uniform marginals to
the marginals of the joint dependence structure is known. This can only be done
for a restricted set of copulas, e.g. a normal copula. Our article introduces
copula-type estimators for flexible multivariate density estimation which also
allow the marginal densities to be modeled separately from the joint
dependence, as in copula modeling, but overcomes the lack of flexibility of
most popular copula estimators. An iterative scheme is proposed for estimating
copula-type estimators and its usefulness is demonstrated through simulation
and real examples. The joint dependence is is modeled by mixture of normals and
mixture of normals factor analyzers models, and mixture of t and mixture of t
factor analyzers models. We develop efficient Variational Bayes algorithms for
fitting these in which model selection is performed automatically. Based on
these mixture models, we construct four classes of copula-type densities which
are far more flexible than current popular copula densities, and outperform
them in simulation and several real data sets.Comment: 27 pages, 3 figure
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