TY - JOUR T1 - On Conditional Applications of Matrix Variate Normal Distribution TT - JF - IJMSI JO - IJMSI VL - 5 IS - 2 UR - http://ijmsi.ir/article-1-139-en.html Y1 - 2010 SP - 33 EP - 43 KW - Bayes estimator KW - Characteristic function KW - Generalized matrix t-distribution KW - Kullback Leibler divergence loss KW - Matrix variate gamma distribution KW - Matrix variate normal distribution. N2 - In this paper, by conditioning on the matrix variate normal distribution (MVND) the construction of the matrix t-type family is considered, thus providing a new perspective of this family. Some important statistical characteristics are given. The presented t-type family is an extension to the work of Dickey [8]. A Bayes estimator for the column covariance matrix &Sigma of MVND is derived under Kullback Leibler divergence loss (KLDL). Further an application of the proposed result is given in the Bayesian context of the multivariate linear model. It is illustrated that the Bayes estimators of coefficient matrix under both SEL and KLDL are identical. M3 10.7508/ijmsi.2010.02.004 ER -