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Discussion of ‘Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan

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  • Paul McNicholas
  • Ryan Browne
  • Paula Murray

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  • Paul McNicholas & Ryan Browne & Paula Murray, 2013. "Discussion of ‘Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 467-472, November.
    • Handle: RePEc:spr:stmapp:v:22:y:2013:i:4:p:467-472
    DOI: 10.1007/s10260-013-0248-1
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    References listed on IDEAS

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    1. Andrews, Jeffrey L. & McNicholas, Paul D. & Subedi, Sanjeena, 2011. "Model-based classification via mixtures of multivariate t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 520-529, January.
    2. Nema Dean & Thomas Brendan Murphy & Gerard Downey, 2006. "Using unlabelled data to update classification rules with applications in food authenticity studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(1), pages 1-14, January.
    3. McLachlan, G.J. & Bean, R.W. & Ben-Tovim Jones, L., 2007. "Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5327-5338, July.
    4. Sharon Lee & Geoffrey McLachlan, 2013. "On mixtures of skew normal and skew $$t$$ -distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 241-266, September.
    5. Tsung-I Lin & Hsiu Ho & Pao Shen, 2009. "Computationally efficient learning of multivariate t mixture models with missing information," Computational Statistics, Springer, vol. 24(3), pages 375-392, August.
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