Add to ORKGLet X have a p-variate normal distribution with mean vector [theta] and identity covariance matrix I. In the squared error estimation of [theta], Baranchik (1970) gives a wide family G of minimax estimators. In this paper, a subfamily C of dominating estimators in G is found such that for each estimator [delta]1 in G not in C, there exists an estimator [delta]2 in C which which dominates [delta]1.admissible Bayes minimax
AbstractThis paper considers the problem of estimating the coefficient matrix B: m × p in a normal m...
AbstractThe problem of minimax estimation of a multivariate normal mean vector has received much att...
AbstractAssume X = (X1, …, Xp)′ is a normal mixture distribution with density w.r.t. Lebesgue measur...
Let X have a p-dimensional normal distribution with mean vector [theta] and identity covariance matr...
AbstractThe problem of estimating the mean of a multivariate normal distribution is considered. A cl...
AbstractThe problem of estimating the mean of a multivariate normal distribution is considered. A cl...
AbstractWe consider estimation of a multivariate normal mean vector under sum of squared error loss....
AbstractIt is well known that the best equivariant estimator of the variance covariance matrix of th...
AbstractBayes estimation of the mean of a variance mixture of multivariate normal distributions is c...
Bayes estimation of the mean of a variance mixture of multivariate normal distributions is considere...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean μ and covariance Σ, and l...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean θ and known covariance ma...
AbstractConsider p independent distributions each belonging to the one parameter exponential family ...
AbstractThe problem of estimating a mean vector of scale mixtures of multivariate normal distributio...
In this paper, the problem of estimating the mean matrix Θ of a matrix-variate normal distribu...
AbstractThis paper considers the problem of estimating the coefficient matrix B: m × p in a normal m...
AbstractThe problem of minimax estimation of a multivariate normal mean vector has received much att...
AbstractAssume X = (X1, …, Xp)′ is a normal mixture distribution with density w.r.t. Lebesgue measur...
Let X have a p-dimensional normal distribution with mean vector [theta] and identity covariance matr...
AbstractThe problem of estimating the mean of a multivariate normal distribution is considered. A cl...
AbstractThe problem of estimating the mean of a multivariate normal distribution is considered. A cl...
AbstractWe consider estimation of a multivariate normal mean vector under sum of squared error loss....
AbstractIt is well known that the best equivariant estimator of the variance covariance matrix of th...
AbstractBayes estimation of the mean of a variance mixture of multivariate normal distributions is c...
Bayes estimation of the mean of a variance mixture of multivariate normal distributions is considere...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean μ and covariance Σ, and l...
AbstractLet X be a p-variate (p ≥ 3) vector normally distributed with mean θ and known covariance ma...
AbstractConsider p independent distributions each belonging to the one parameter exponential family ...
AbstractThe problem of estimating a mean vector of scale mixtures of multivariate normal distributio...
In this paper, the problem of estimating the mean matrix Θ of a matrix-variate normal distribu...
AbstractThis paper considers the problem of estimating the coefficient matrix B: m × p in a normal m...
AbstractThe problem of minimax estimation of a multivariate normal mean vector has received much att...
AbstractAssume X = (X1, …, Xp)′ is a normal mixture distribution with density w.r.t. Lebesgue measur...