Add to ORKGFor Wishart density functions, we study the risk dominance problems of the restricted maximum likelihood estimators of mean matrices with respect to the Kullback-Leibler loss function over restricted parameter space under the simple tree ordering set. The results are directly applied to the estimation of covariance matrices for the completely balanced multivariate multi-way random effects models without interactions.Kullback-Leibler loss Maximum likelihood estimators Risk dominance Simple tree ordering set Wishart density function
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean μ and covariance Σ, and l...
AbstractWe consider the asymptotic joint distribution of the eigenvalues and eigenvectors of Wishart...
The problem of estimating large covariance matrices of multivariate real normal and complex normal d...
AbstractFor Wishart density functions, we study the risk dominance problems of the restricted maximu...
AbstractFor Wishart density functions, there remains a long-time question unsolved. That is whether ...
AbstractThe closed-form maximum likelihood estimators for the completely balanced multivariate one-w...
The closed-form maximum likelihood estimators for the completely balanced multivariate one-way rando...
The estimation of the precision matrix of the Wishart distribution is one of classical problems stud...
Let S: p - p have a nonsingular Wishart distribution with unknown matrix [Sigma] and n degrees of fr...
AbstractIn this paper the problem of estimating a covariance matrix parametrized by an irreducible s...
AbstractLet Sp×p have a Wishart distribution with unknown matrix Σ and k degrees of freedom. For a m...
This paper tabulates the distribution of the largest and smallest characteristic roots of a Wishart ...
summary:In a multivariate normal distribution, let the inverse of the covariance matrix be a band ma...
Let X be an m - p matrix normally distributed with matrix of means B and covariance matrix Im [circl...
ABSTRACT. In this paper, we obtain a property of the expectation of the inverse of compound Wishart ...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean μ and covariance Σ, and l...
AbstractWe consider the asymptotic joint distribution of the eigenvalues and eigenvectors of Wishart...
The problem of estimating large covariance matrices of multivariate real normal and complex normal d...
AbstractFor Wishart density functions, we study the risk dominance problems of the restricted maximu...
AbstractFor Wishart density functions, there remains a long-time question unsolved. That is whether ...
AbstractThe closed-form maximum likelihood estimators for the completely balanced multivariate one-w...
The closed-form maximum likelihood estimators for the completely balanced multivariate one-way rando...
The estimation of the precision matrix of the Wishart distribution is one of classical problems stud...
Let S: p - p have a nonsingular Wishart distribution with unknown matrix [Sigma] and n degrees of fr...
AbstractIn this paper the problem of estimating a covariance matrix parametrized by an irreducible s...
AbstractLet Sp×p have a Wishart distribution with unknown matrix Σ and k degrees of freedom. For a m...
This paper tabulates the distribution of the largest and smallest characteristic roots of a Wishart ...
summary:In a multivariate normal distribution, let the inverse of the covariance matrix be a band ma...
Let X be an m - p matrix normally distributed with matrix of means B and covariance matrix Im [circl...
ABSTRACT. In this paper, we obtain a property of the expectation of the inverse of compound Wishart ...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean μ and covariance Σ, and l...
AbstractWe consider the asymptotic joint distribution of the eigenvalues and eigenvectors of Wishart...
The problem of estimating large covariance matrices of multivariate real normal and complex normal d...