Abstract:
This paper introduces a new family of matrix variate distributions based on the mean-mixture
of normal (MMN) models. The properties of the new matrix variate family, namely stochastic
representation, moments and characteristic function, linear and quadratic forms as
well as marginal and conditional distributions are investigated. Three special cases including
the restricted skew-normal, exponentiated MMN and the mixed-Weibull MMN matrix variate
distributions are presented and studied. Based on the specific presentation of the proposed
model, an EM-type algorithm can be directly implemented for obtaining maximum likelihood
estimate of the parameters. The usefulness and practical utility of the proposed methodology
are illustrated through two conducted simulation studies and through the Landsat satellite
dataset analysis.
