Mixtures of Hidden Truncation Hyperbolic Factor Analyzers

Paula M. Murray, Ryan P. Browne () and Paul D. McNicholas ()
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Paula M. Murray: McMaster University
Ryan P. Browne: University of Waterloo
Paul D. McNicholas: McMaster University

Journal of Classification, 2020, vol. 37, issue 2, No 7, 366-379

Abstract: Abstract The mixture of factor analyzers model was first introduced over 20 years ago and, in the meantime, has been extended to several non-Gaussian analogs. In general, these analogs account for situations with heavy tailed and/or skewed clusters. An approach is introduced that unifies many of these approaches into one very general model: the mixture of hidden truncation hyperbolic factor analyzers (MHTHFA) model. In the process of doing this, a hidden truncation hyperbolic factor analysis model is also introduced. The MHTHFA model is illustrated for clustering as well as semi-supervised classification using two real datasets.

Keywords: Hidden truncation hyperbolic distribution; Hidden truncation hyperbolic factor analysis; MHTHFA; Mixture of factor analyzers; Mixture of hidden truncation hyperbolic factor analyzers (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00357-019-9309-y

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