Scale and shape mixtures of matrix variate extended skew normal distributions

Amir Rezaei, Fatemeh Yousefzadeh and Reinaldo B. Arellano-Valle

Journal of Multivariate Analysis, 2020, vol. 179, issue C

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.

Keywords: EM algorithm; Matrix variate distributions; Matrix variate tail conditional expectation; Maximum likelihood estimator; Scale and shape mixtures; Skew normal distribution (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2020.104649

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