Skew-normal Mean-variance Mixture of Birnbaum-Saunders Distribution | Zendy

Mostafa Tamandi | Zendy; Hossein Negarestani | Zendy; Ahad Jamalizadeh | Zendy; Mehdi Amiri | Zendy

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Skew-normal Mean-variance Mixture of Birnbaum-Saunders Distribution

Author(s) -

Mostafa Tamandi,

Hossein Negarestani,

Ahad Jamalizadeh,

Mehdi Amiri

Publication year - 2019

Publication title -

journal of the iranian statistical society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.293

H-Index - 6

eISSN - 2538-189X

pISSN - 1726-4057

DOI - 10.29252/jirss.18.2.87

Subject(s) - skew , mathematics , statistics , skew normal distribution , variance (accounting) , normal distribution , inference , econometrics , artificial intelligence , computer science , telecommunications , accounting , business

This paper presents a skew-normal mean-variance mixture based on BirnbaumSaunders (SNMVBS) distribution and discusses some of its key properties. The SNMVBS distribution can be thought as a flexible extension of the normal mean-variance mixture based on Birnbaum-Saunders (NMVBS) distribution as it possesses one additional shape parameter for providing more flexibility with skewness and kurtosis. Next, we develop a computationally feasible ECM algorithm for the maximum likelihood estimation of the model parameters. Asymptotic standard errors of the ML estimates are obtained through an approximation of the observed information matrix. Finally, the usefulness of the proposed model and its fitting method are illustrated through a Monte-Carlo simulation as well as three real-life datasets.

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