A mixture of generalized hyperbolic factor analyzers

Cristina Tortora (), Paul D. McNicholas () and Ryan P. Browne ()
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Cristina Tortora: McMaster University
Paul D. McNicholas: McMaster University
Ryan P. Browne: McMaster University

Advances in Data Analysis and Classification, 2016, vol. 10, issue 4, No 2, 423-440

Abstract: Abstract The mixture of factor analyzers model, which has been used successfully for the model-based clustering of high-dimensional data, is extended to generalized hyperbolic mixtures. The development of a mixture of generalized hyperbolic factor analyzers is outlined, drawing upon the relationship with the generalized inverse Gaussian distribution. An alternating expectation-conditional maximization algorithm is used for parameter estimation, and the Bayesian information criterion is used to select the number of factors as well as the number of components. The performance of our generalized hyperbolic factor analyzers model is illustrated on real and simulated data, where it performs favourably compared to its Gaussian analogue and other approaches.

Keywords: Clustering; Generalized hyperbolic distribution; Mixture of factor analyzers; AECM algorithm; 62H30; 62F99 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s11634-015-0204-z

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