Mixtures of multivariate restricted skew-normal factor analyzer models in a Bayesian framework

Mohsen Maleki and Darren Wraith ()
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Mohsen Maleki: Shiraz University
Darren Wraith: Queensland University of Technology (QUT)

Computational Statistics, 2019, vol. 34, issue 3, No 6, 1039-1053

Abstract: Abstract The mixture of factor analyzers (MFA) model, by reducing the number of free parameters through its factor-analytic representation of the component covariance matrices, is an important statistical model to identify hidden or latent groups in high dimensional data. Recent approaches to extend the approach to skewed data or skewness in the latent groups have been examined in a frequentist setting where there are some known computational limitations. For these reasons we consider a Bayesian approach to the restricted skew-normal mixtures of factor analysis MFA model. We examine the performance and flexibility of the approach on real datasets and illustrate some of the computational advantages in a missing data setting.

Keywords: Bayesian analysis; Gibbs sampling; Mixture of factor analysis model; Restricted skew-normal distribution (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s00180-019-00870-6

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