ABSTRACT In this article, we introduce a matrix‐variate skew normal distribution and its extended version for modeling asymmetric matrix‐valued data. We investigate the main theoretical properties of these models and develop an EM‐type algorithm for maximum likelihood estimation. The proposed methodology is assessed through simulation studies and illustrated with an application to historical Dow Jones dividend data, highlighting its practical relevance for modeling asymmetric dependence structures.
Correia et al. (Fri,) studied this question.