AbstractFor Wishart density functions, there remains a long-time question unsolved. That is whether there exists the closed-form MLEs of mean matrices over the partially Löwner ordering sets. In this note, we provide an affirmative answer by demonstrating a unified procedure on exactly how the closed-form MLEs are obtained for the simple ordering case. Under the Kullback–Leibler loss function, a property of obtained MLEs is further studied. Some applications of the obtained closed-form MLEs, including the comparison between our ML estimates and Calvin and Dykstra's [Maximum likelihood estimation of a set of covariance matrices under Löwner order restrictions with applications to balanced multivariate variance components models, Ann. Statist...
This article is motivated by the difficulty of applying standard simulation techniques when identifi...
We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
AbstractFor Wishart density functions, there remains a long-time question unsolved. That is whether ...
For Wishart density functions, we study the risk dominance problems of the restricted maximum likeli...
AbstractFor Wishart density functions, we study the risk dominance problems of the restricted maximu...
The closed-form maximum likelihood estimators for the completely balanced multivariate one-way rando...
AbstractThe closed-form maximum likelihood estimators for the completely balanced multivariate one-w...
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...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean μ and covariance Σ, and l...
AbstractLet S: p × p have a nonsingular Wishart distribution with unknown matrix Σ and n degrees of ...
AbstractThis paper provides an exposition of alternative approaches for obtaining maximum- likelihoo...
This article is motivated by the difficulty of applying standard simulation techniques when iden-tif...
We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian...
This article is motivated by the difficulty of applying standard simulation techniques when identifi...
We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
AbstractFor Wishart density functions, there remains a long-time question unsolved. That is whether ...
For Wishart density functions, we study the risk dominance problems of the restricted maximum likeli...
AbstractFor Wishart density functions, we study the risk dominance problems of the restricted maximu...
The closed-form maximum likelihood estimators for the completely balanced multivariate one-way rando...
AbstractThe closed-form maximum likelihood estimators for the completely balanced multivariate one-w...
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...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean μ and covariance Σ, and l...
AbstractLet S: p × p have a nonsingular Wishart distribution with unknown matrix Σ and n degrees of ...
AbstractThis paper provides an exposition of alternative approaches for obtaining maximum- likelihoo...
This article is motivated by the difficulty of applying standard simulation techniques when iden-tif...
We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian...
This article is motivated by the difficulty of applying standard simulation techniques when identifi...
We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...