Authors | Fatemeh Yousefzadeh,Reinaldo B. Arellano-Valle |
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Journal | Journal of Multivariate Analysis |
Page number | 1-17 |
Serial number | 179 |
Volume number | 10 |
IF | 0.773 |
Paper Type | Full Paper |
Published At | 2020 |
Journal Grade | ISI |
Journal Type | Typographic |
Journal Country | Iran, Islamic Republic Of |
Journal Index | JCR،Scopus |
Abstract
In this paper, we propose a matrix extension of the scale and shape mixtures of multivariate skew normal distributions and present some particular cases of this new class. We also present several formal properties of this class, such as the marginal distributions, the moment generating function, the distribution of linear and quadratic forms, and the selection and stochastic representations. In addition, we introduce the matrix variate tail conditional expectation measure and derive this risk measure for the scale and shape mixtures of matrix variate extended skew normal distributions. We present an efficient EM-type algorithm for the computation of maximum likelihood estimates of parameters in some special cases of the proposed class. Finally, we conduct a small simulation study and fit various special cases of the new class to a real dataset.
tags: EM algorithm Matrix variate distributions Matrix variate tail conditional expectation Maximum likelihood estimator Scale and shape mixtures Skew normal distribution