McNicholas, M. S, McNicholas, D. P, Browne, P. R (2017). A Mixture of Variance-Gamma Factor Analyzers. Springer International Publishing, Cham. ISBN 978-3-319-41573-4, doi:10.1007/978-3-319-41573-4_18, https://doi.org/10.1007/978-3-319-41573-4_18.

Corresponding BibTeX entry:

  @Book{,
    author = {{McNicholas} and Sharon M. and {McNicholas} and Paul D.
      and {Browne} and Ryan P.},
    editor = {{Ahmed} and S. Ejaz},
    title = {A Mixture of Variance-Gamma Factor Analyzers},
    booktitle = {Big and Complex Data Analysis: Methodologies and
      Applications},
    year = {2017},
    publisher = {Springer International Publishing},
    address = {Cham},
    pages = {369--385},
    abstract = {The mixture of factor analyzers model is extended to
      variance-gamma mixtures to facilitate flexible clustering of
      high-dimensional data. The formation of the variance-gamma
      distribution utilized is a special and limiting case of the
      generalized hyperbolic distribution. Parameter estimation for
      these mixtures is carried out via an alternating
      expectation-conditional maximization algorithm, and relies on
      convenient expressions for expected values for the generalized
      inverse Gaussian distribution. The Bayesian information criterion
      is used to select the number of latent factors. The mixture of
      variance-gamma factor analyzers model is illustrated on a
      well-known breast cancer data set. Finally, the place of
      variance-gamma mixtures within the growing body of literature on
      non-Gaussian mixtures is considered.},
    isbn = {978-3-319-41573-4},
    doi = {10.1007/978-3-319-41573-4_18},
    url = {https://doi.org/10.1007/978-3-319-41573-4_18},
  }