Multivariate restricted skew-normal scale mixture of Birnbaum-Saunders distribution

Hossein Samary, Zahra Khodadadi, Hedieh Jafarpour


In spite of widespread use as well as theoretical properties of the multivariate scale mixture normal distributions, practical studies show a lack of stability and robustness against asymmetric features such as asymmetry and heavy tails. In this paper, we develop a new multivariate model by assuming the Birnbaum-Saunders distribution for the mixing variable in the scale mix- tures restricted skew-normal distribution. An analytically simple and efficient EM-type algorithm is adopted for iteratively computing maximum likelihood estimate of model parameters. To account standard errors, the observed in- formation matrix is derived analytically by offering an information-based ap-proach. Results obtained from real and simulated datasets are reported toillustrate the practical utility of the proposed methodology.


EM-type algorithm, Birnbaum-Saunders distribu- tion, Multivariate scale mixture distribution, Restricted skew-normal distribu- tion.


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