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

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  • Mohsen Maleki

    (Shiraz University)

  • Darren Wraith

    (Queensland University of Technology (QUT))

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.

Suggested Citation

  • Mohsen Maleki & Darren Wraith, 2019. "Mixtures of multivariate restricted skew-normal factor analyzer models in a Bayesian framework," Computational Statistics, Springer, vol. 34(3), pages 1039-1053, September.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:3:d:10.1007_s00180-019-00870-6
    DOI: 10.1007/s00180-019-00870-6
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    Cited by:

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    2. Atefeh Zarei & Zahra Khodadadi & Mohsen Maleki & Karim Zare, 2023. "Robust mixture regression modeling based on two-piece scale mixtures of normal 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. 17(1), pages 181-210, March.
    3. Kim, Nam-Hwui & Browne, Ryan P., 2021. "In the pursuit of sparseness: A new rank-preserving penalty for a finite mixture of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    4. Lee, Sharon X. & McLachlan, Geoffrey J., 2022. "An overview of skew distributions in model-based clustering," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    5. Wan-Lun Wang & Tsung-I Lin, 2022. "Robust clustering via mixtures of t factor analyzers with incomplete data," 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. 16(3), pages 659-690, September.

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