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Likelihood-based inference for multivariate skew scale mixtures of normal distributions

Author

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  • Clécio S. Ferreira

    (Federal University of Juiz de Fora)

  • Víctor H. Lachos

    (Universidade Estadual de Campinas)

  • Heleno Bolfarine

    (Universidade de São Paulo)

Abstract

Scale mixtures of normal distributions are often used as a challenging class for statistical analysis of symmetrical data. Recently, Ferreira et al. (Stat Methodol 8:154–171, 2011) defined the univariate skew scale mixtures of normal distributions that offer much needed flexibility by combining both skewness with heavy tails. In this paper, we develop a multivariate version of the skew scale mixtures of normal distributions, with emphasis on the multivariate skew-Student-t, skew-slash and skew-contaminated normal distributions. The main virtue of the members of this family of distributions is that they are easy to simulate from and they also supply genuine expectation/conditional maximisation either algorithms for maximum likelihood estimation. The observed information matrix is derived analytically to account for standard errors. Results obtained from real and simulated datasets are reported to illustrate the usefulness of the proposed method.

Suggested Citation

  • Clécio S. Ferreira & Víctor H. Lachos & Heleno Bolfarine, 2016. "Likelihood-based inference for multivariate skew scale mixtures of normal distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(4), pages 421-441, October.
    • Handle: RePEc:spr:alstar:v:100:y:2016:i:4:d:10.1007_s10182-016-0266-z
    DOI: 10.1007/s10182-016-0266-z
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    References listed on IDEAS

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    2. Cabral, Celso Rômulo Barbosa & Lachos, Víctor Hugo & Zeller, Camila Borelli, 2014. "Multivariate measurement error models using finite mixtures of skew-Student t distributions," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 179-198.
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    Cited by:

    1. Akram Hoseinzadeh & Mohsen Maleki & Zahra Khodadadi, 2021. "Heteroscedastic nonlinear regression models using asymmetric and heavy tailed two-piece distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 451-467, September.
    2. Graciliano M. S. Louredo & Camila B. Zeller & Clécio S. Ferreira, 2022. "Estimation and Influence Diagnostics for the Multivariate Linear Regression Models with Skew Scale Mixtures of Normal Distributions," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 204-242, May.
    3. Abe, Toshihiro & Fujisawa, Hironori & Kawashima, Takayuki & Ley, Christophe, 2021. "EM algorithm using overparameterization for the multivariate skew-normal distribution," Econometrics and Statistics, Elsevier, vol. 19(C), pages 151-168.
    4. Hok Shing Kwong & Saralees Nadarajah, 2022. "A New Robust Class of Skew Elliptical Distributions," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1669-1691, September.
    5. Arellano-Valle, Reinaldo B. & Ferreira, Clécio S. & Genton, Marc G., 2018. "Scale and shape mixtures of multivariate skew-normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 98-110.
    6. 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.

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