114 research outputs found
Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions
Families of mixtures of multivariate power exponential (MPE) distributions
have been previously introduced and shown to be competitive for cluster
analysis in comparison to other elliptical mixtures including mixtures of
Gaussian distributions. Herein, we propose a family of mixtures of multivariate
skewed power exponential distributions to combine the flexibility of the MPE
distribution with the ability to model skewness. These mixtures are more robust
to variations from normality and can account for skewness, varying tail weight,
and peakedness of data. A generalized expectation-maximization approach
combining minorization-maximization and optimization based on accelerated line
search algorithms on the Stiefel manifold is used for parameter estimation.
These mixtures are implemented both in the model-based clustering and
classification frameworks. Both simulated and benchmark data are used for
illustration and comparison to other mixture families
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