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
AbstractSome lower bounds for the variance of a function g of a random vector X are extended to a wi...
AbstractSome lower bounds for the variance of a function g of a random vector X are extended to a wi...
The matrix inequality (*).i f- n ' ~.i,- rr ' is considered, where t and r are positive s...
Matrix algebra is extensively used in the study of linear models and multivariate analysis (see for ...
AbstractMatrix algebra is extensively used in the study of linear models and multivariate analysis (...
Books on linear models and multivariate analysis generally include a chapter on matrix algebra, quit...
AbstractThis expository paper describes the ways in which a matrix theoretic construct called the Sc...
In this survey paper, a summary of results which are to be found in a series of papers, is presented...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a ...
Abstract. The purpose of this paper is to revisit Hua’s matrix equality (and inequality) through the...
Summary. The Fisher Information Matrix formalism (Fisher, 1935) is extended to cases where both elem...
We derive fundamental constraints for the Schur complement of positive matrices, which provide an op...
ABSTRACT. In this note, we review score functions properties and discuss inequalities on the Fisher ...
Some lower bounds for the variance of a function g of a random vector X are extended to a wider clas...
AbstractStyan [G.P.H. Styan, Hadamard products and multivariate statistical analysis. Linear Algebra...
AbstractSome lower bounds for the variance of a function g of a random vector X are extended to a wi...
AbstractSome lower bounds for the variance of a function g of a random vector X are extended to a wi...
The matrix inequality (*).i f- n ' ~.i,- rr ' is considered, where t and r are positive s...
Matrix algebra is extensively used in the study of linear models and multivariate analysis (see for ...
AbstractMatrix algebra is extensively used in the study of linear models and multivariate analysis (...
Books on linear models and multivariate analysis generally include a chapter on matrix algebra, quit...
AbstractThis expository paper describes the ways in which a matrix theoretic construct called the Sc...
In this survey paper, a summary of results which are to be found in a series of papers, is presented...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a ...
Abstract. The purpose of this paper is to revisit Hua’s matrix equality (and inequality) through the...
Summary. The Fisher Information Matrix formalism (Fisher, 1935) is extended to cases where both elem...
We derive fundamental constraints for the Schur complement of positive matrices, which provide an op...
ABSTRACT. In this note, we review score functions properties and discuss inequalities on the Fisher ...
Some lower bounds for the variance of a function g of a random vector X are extended to a wider clas...
AbstractStyan [G.P.H. Styan, Hadamard products and multivariate statistical analysis. Linear Algebra...
AbstractSome lower bounds for the variance of a function g of a random vector X are extended to a wi...
AbstractSome lower bounds for the variance of a function g of a random vector X are extended to a wi...
The matrix inequality (*).i f- n ' ~.i,- rr ' is considered, where t and r are positive s...