AbstractMatrix algebra is extensively used in the study of linear models and multivariate analysis (see for instance Refs. [18,21]). During recent years, there have been a number of papers where statistical results are used to prove some matrix theorems, especially matrix inequalities (Refs. [5,7,8,10,14,15]). In this paper, a number of matrix results are proved using some properties of Fisher information and covariance matrices. A unified approach is provided through the use of Schur complements. It may be noted that the statistical results used are derivable without using matrix theory
AbstractThis expository paper describes the ways in which a matrix theoretic construct called the Sc...
AbstractConsider the multivariate linear model for the random matrixYn×p∼MN(XB,V⊗Σ), whereBis the pa...
AbstractThe main result provides a matrix generalization of a quadratic inequality for real numbers....
AbstractMatrix algebra is extensively used in the study of linear models and multivariate analysis (...
Matrix algebra is extensively used in the study of linear models and multivariate analysis (see for ...
AbstractStyan [G.P.H. Styan, Hadamard products and multivariate statistical analysis. Linear Algebra...
Books on linear models and multivariate analysis generally include a chapter on matrix algebra, quit...
AbstractStyan [G.P.H. Styan, Hadamard products and multivariate statistical analysis. Linear Algebra...
summary:Let two random vectors $X_1$ and $X_2$ be jointly distributed as a normal distribution with ...
AbstractIf x and y are nonnegative vectors of order n, and if Σni = 1xi = Σni = 1yi, then a well-kno...
AbstractIt has long been known that an analogue of Jensen’s inequality holds for positive unital lin...
In this dissertation we assume that the observations are from normal populations but are correlated ...
In this dissertation we assume that the observations are from normal populations but are correlated ...
AbstractNew inequalities for eigenvalues of matrices are obtained. They make Schur's and Brown's the...
AbstractIn this paper we discuss various properties of matrices of the type S=H−GE−1F, which we call...
AbstractThis expository paper describes the ways in which a matrix theoretic construct called the Sc...
AbstractConsider the multivariate linear model for the random matrixYn×p∼MN(XB,V⊗Σ), whereBis the pa...
AbstractThe main result provides a matrix generalization of a quadratic inequality for real numbers....
AbstractMatrix algebra is extensively used in the study of linear models and multivariate analysis (...
Matrix algebra is extensively used in the study of linear models and multivariate analysis (see for ...
AbstractStyan [G.P.H. Styan, Hadamard products and multivariate statistical analysis. Linear Algebra...
Books on linear models and multivariate analysis generally include a chapter on matrix algebra, quit...
AbstractStyan [G.P.H. Styan, Hadamard products and multivariate statistical analysis. Linear Algebra...
summary:Let two random vectors $X_1$ and $X_2$ be jointly distributed as a normal distribution with ...
AbstractIf x and y are nonnegative vectors of order n, and if Σni = 1xi = Σni = 1yi, then a well-kno...
AbstractIt has long been known that an analogue of Jensen’s inequality holds for positive unital lin...
In this dissertation we assume that the observations are from normal populations but are correlated ...
In this dissertation we assume that the observations are from normal populations but are correlated ...
AbstractNew inequalities for eigenvalues of matrices are obtained. They make Schur's and Brown's the...
AbstractIn this paper we discuss various properties of matrices of the type S=H−GE−1F, which we call...
AbstractThis expository paper describes the ways in which a matrix theoretic construct called the Sc...
AbstractConsider the multivariate linear model for the random matrixYn×p∼MN(XB,V⊗Σ), whereBis the pa...
AbstractThe main result provides a matrix generalization of a quadratic inequality for real numbers....