Scale and shape mixtures of matrix variate extended skew normal distributions

JOURNAL OF MULTIVARIATE ANALYSIS

卷:179

Article

Rezaei, Amir¹  Yousefzadeh, Fatemeh¹  Arellano-Valle, Reinaldo B.² 

[1] Univ Birjand, Fac Math & Stat, Dept Stat, Birjand, Iran

[2] Pontificia Univ Catolica Chile, Dept Estadist, Santiago, Chile

关键词: EM algorithm;    Matrix variate distributions;    Matrix variate tail conditional expectation;    Maximum likelihood estimator;    Scale and shape mixtures;    Skew normal distribution;   

DOI  :  10.1016/j.jmva.2020.104649

来源: Elsevier

【 摘 要 】

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. (C) 2020 Published by Elsevier Inc.

【 授权许可】

Free