Let X be distributed as matrix normal with mean M and covariance matrix W⊗V, where W and V are nonnegative definite (nnd) matrices. In this paper we present a simple version of the Cochran’s theorem for matrix quadratic forms in X. The theorem is used to characterize the class of nnd matrices W such that the matrix quadratic forms that occur in multivariate analysis of variance are independent and Wishart except for a scale factor. © 2003 Elsevier Inc. All rights reserved
This thesis is divided into two related parts: (I) Moments. For a multivariate elliptically contoure...
AbstractConsider the multivariate linear model for the random matrixYn×p∼MN(XB,V⊗Σ), whereBis the pa...
AbstractLet A be a symmetric matrix and B be a nonnegative definite (nnd) matrix. We obtain a charac...
Let X be distributed as matrix normal with mean M and covariance matrix W⊗V, where W and V are nonne...
AbstractLet X be distributed as matrix normal with mean M and covariance matrix W⊗V, where W and V a...
AbstractLet Y be an n×p multivariate normal random matrix with general covariance ΣY. The general co...
Let Y be an nxp multivariate normal random matrix with general covariance [Sigma]Y. The general cova...
AbstractFor a normal random matrix Y with mean zero, necessary and sufficient conditions are obtaine...
AbstractLet Y be an n×p multivariate normal random matrix with general covariance ΣY and W be a symm...
For a normally distributed random matrixYwith a general variance-covariance matrix[Sigma]Y, and for ...
For a normal random matrix Y with mean zero, necessary and sufficient conditions are obtained for Y'...
AbstractFor a normally distributed random matrixYwith a general variance–covariance matrixΣY, and fo...
AbstractLet M be a random symmetric real p-matrix of Wishart distribution with k degrees of freedom ...
[[abstract]]For a normal random variable Y with mean zero and general covari- ance matrix Y the Wish...
AbstractA general easily verifiable Cochran theorem is obtained for a normal random matrix Y with me...
This thesis is divided into two related parts: (I) Moments. For a multivariate elliptically contoure...
AbstractConsider the multivariate linear model for the random matrixYn×p∼MN(XB,V⊗Σ), whereBis the pa...
AbstractLet A be a symmetric matrix and B be a nonnegative definite (nnd) matrix. We obtain a charac...
Let X be distributed as matrix normal with mean M and covariance matrix W⊗V, where W and V are nonne...
AbstractLet X be distributed as matrix normal with mean M and covariance matrix W⊗V, where W and V a...
AbstractLet Y be an n×p multivariate normal random matrix with general covariance ΣY. The general co...
Let Y be an nxp multivariate normal random matrix with general covariance [Sigma]Y. The general cova...
AbstractFor a normal random matrix Y with mean zero, necessary and sufficient conditions are obtaine...
AbstractLet Y be an n×p multivariate normal random matrix with general covariance ΣY and W be a symm...
For a normally distributed random matrixYwith a general variance-covariance matrix[Sigma]Y, and for ...
For a normal random matrix Y with mean zero, necessary and sufficient conditions are obtained for Y'...
AbstractFor a normally distributed random matrixYwith a general variance–covariance matrixΣY, and fo...
AbstractLet M be a random symmetric real p-matrix of Wishart distribution with k degrees of freedom ...
[[abstract]]For a normal random variable Y with mean zero and general covari- ance matrix Y the Wish...
AbstractA general easily verifiable Cochran theorem is obtained for a normal random matrix Y with me...
This thesis is divided into two related parts: (I) Moments. For a multivariate elliptically contoure...
AbstractConsider the multivariate linear model for the random matrixYn×p∼MN(XB,V⊗Σ), whereBis the pa...
AbstractLet A be a symmetric matrix and B be a nonnegative definite (nnd) matrix. We obtain a charac...