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

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.