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 -