RT - Journal Article
T1 - On Conditional Applications of Matrix Variate Normal Distribution
JF - IJMSI
YR - 2010
JO - IJMSI
VO - 5
IS - 2
UR - http://ijmsi.ir/article-1-139-en.html
SP - 33
EP - 43
K1 - Bayes estimator
K1 - Characteristic function
K1 - Generalized matrix t-distribution
K1 - Kullback Leibler divergence loss
K1 - Matrix variate gamma distribution
K1 - Matrix variate normal distribution.
AB - 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.
LA eng
UL http://ijmsi.ir/article-1-139-en.html
M3 10.7508/ijmsi.2010.02.004
ER -