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A robust class of multivariate fatigue distributions based on normal mean-variance mixture model

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Abstract

The Birnbaum–Saunders (BS) distribution, introduced in 1969, is a popular univariate fatigue life distribution which has been widely used to model right-skewed lifetime and reliability data. In this paper, a new class of generalized multivariate BS distributions is proposed based on mean-variance mixture models to accommodate strongly skewed and heavy tailed multivariate lifetime data. Some special cases of this class as well as their properties are then discussed. We present a hierarchical representation which facilitates an efficient EM-type algorithm for the computation of maximum likelihood estimates. Empirical results from a simulation study and real data analyses show that this class of distributions outperforms many existing extensions of the BS distribution in modeling lifetime data.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

    Mahsa Sasaei, Reza Pourmousa & Ahad Jamalizadeh

  2. Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada

    Narayanaswamy Balakrishnan

Authors
  1. Mahsa Sasaei
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  2. Reza Pourmousa
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  3. Narayanaswamy Balakrishnan
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  4. Ahad Jamalizadeh
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Correspondence to Reza Pourmousa.

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Sasaei, M., Pourmousa, R., Balakrishnan, N. et al. A robust class of multivariate fatigue distributions based on normal mean-variance mixture model. J. Korean Stat. Soc. 50, 44–68 (2021). https://doi.org/10.1007/s42952-020-00063-8

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  • DOI: https://doi.org/10.1007/s42952-020-00063-8

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