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A skew factor analysis model based on the normal mean–variance mixture of Birnbaum–Saunders distribution

Author

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  • Farzane Hashemi
  • Mehrdad Naderi
  • Ahad Jamalizadeh
  • Tsung-I Lin

Abstract

This paper presents a robust extension of factor analysis model by assuming the multivariate normal mean–variance mixture of Birnbaum–Saunders distribution for the unobservable factors and errors. A computationally analytical EM-based algorithm is developed to find maximum likelihood estimates of the parameters. The asymptotic standard errors of parameter estimates are derived under an information-based paradigm. Numerical merits of the proposed methodology are illustrated using both simulated and real datasets.

Suggested Citation

  • Farzane Hashemi & Mehrdad Naderi & Ahad Jamalizadeh & Tsung-I Lin, 2020. "A skew factor analysis model based on the normal mean–variance mixture of Birnbaum–Saunders distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(16), pages 3007-3029, December.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:16:p:3007-3029
    DOI: 10.1080/02664763.2019.1709054
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    Cited by:

    1. Hashemi, Farzane & Naderi, Mehrdad & Jamalizadeh, Ahad & Bekker, Andriette, 2021. "A flexible factor analysis based on the class of mean-mixture of normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. 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.

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