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Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions

Author

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  • Wan-Lun Wang

    (Feng Chia University)

  • Ahad Jamalizadeh

    (Shahid Bahonar University of Kerman
    Mahani Mathematical Research Center, Shahid Bahonar University of Kerman)

  • Tsung-I Lin

    (National Chung Hsing University
    China Medical University)

Abstract

Finite mixtures of multivariate skew distributions have become increasingly popular in recent years due to their flexibility and robustness in modeling heterogeneity, asymmetry and leptokurticness of the data. This paper introduces a novel finite mixture of multivariate scale-shape mixtures of skew-normal distributions to enhance strength and flexibility when modeling heterogeneous multivariate data that contain more extreme non-normal features. A computational tractable ECM algorithm which consists of analytically simple E- and CM-steps is developed to carry out maximum likelihood estimation of parameters. The asymptotic covariance matrix of parameter estimates is derived from the observed information matrix using the outer product of expected complete-data scores. We demonstrate the utility of the proposed approach through simulated and real data examples.

Suggested Citation

  • Wan-Lun Wang & Ahad Jamalizadeh & Tsung-I Lin, 2020. "Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions," Statistical Papers, Springer, vol. 61(6), pages 2643-2670, December.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:6:d:10.1007_s00362-018-01061-z
    DOI: 10.1007/s00362-018-01061-z
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    References listed on IDEAS

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    Cited by:

    1. Tsung-I Lin & I-An Chen & Wan-Lun Wang, 2023. "A robust factor analysis model based on the canonical fundamental skew-t distribution," Statistical Papers, Springer, vol. 64(2), pages 367-393, April.
    2. Abbas Mahdavi & Vahid Amirzadeh & Ahad Jamalizadeh & Tsung-I Lin, 2021. "Maximum likelihood estimation for scale-shape mixtures of flexible generalized skew normal distributions via selection representation," Computational Statistics, Springer, vol. 36(3), pages 2201-2230, September.

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