AbstractThe paper develops a general class of shrinkage estimators for estimating the normal mean, which dominates the sample mean in three or higher dimensions under a general divergence loss. In the process, the earlier works of James and Stein [11] and Efron and Morris [5] are generalized considerably
AbstractIn this paper we propose James–Stein type estimators for variances raised to a fixed power b...
AbstractThe problem of estimating large covariance matrices of multivariate real normal and complex ...
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in h...
Consider a p-variate(p ≥ 3) normal distribution with mean and covariance matrix Σ = 2I p for any un...
We consider the estimation of the mean of a multivariate normal distribution with known variance. Mo...
In this paper, we are interested in estimating a multivariate normal mean under the balanced loss fu...
This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal d...
Estimating a covariance matrix is an important task in applications where the number of vari-ables i...
AbstractWe establish the Stein phenomenon in the context of two-step, monotone incomplete data drawn...
Consider estimating an n×p matrix of means Θ, say, from an n×p matrix of observations X, where the e...
The problem of estimating large covariance matrices of multivariate real normal and complex normal d...
This paper considers the problem of estimating a high-dimensional vector θ ∈ ℝn from a noisy one-tim...
Consider the problem of estimating the mean vector [theta] of a random variable X in , with a spheri...
The Reverse Stein Eect is identied and illustrated: A statistician who shrinks his/her data toward a...
In estimating a multivariate normal mean, both the celebrated James-Stein estimator and the Bayes es...
AbstractIn this paper we propose James–Stein type estimators for variances raised to a fixed power b...
AbstractThe problem of estimating large covariance matrices of multivariate real normal and complex ...
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in h...
Consider a p-variate(p ≥ 3) normal distribution with mean and covariance matrix Σ = 2I p for any un...
We consider the estimation of the mean of a multivariate normal distribution with known variance. Mo...
In this paper, we are interested in estimating a multivariate normal mean under the balanced loss fu...
This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal d...
Estimating a covariance matrix is an important task in applications where the number of vari-ables i...
AbstractWe establish the Stein phenomenon in the context of two-step, monotone incomplete data drawn...
Consider estimating an n×p matrix of means Θ, say, from an n×p matrix of observations X, where the e...
The problem of estimating large covariance matrices of multivariate real normal and complex normal d...
This paper considers the problem of estimating a high-dimensional vector θ ∈ ℝn from a noisy one-tim...
Consider the problem of estimating the mean vector [theta] of a random variable X in , with a spheri...
The Reverse Stein Eect is identied and illustrated: A statistician who shrinks his/her data toward a...
In estimating a multivariate normal mean, both the celebrated James-Stein estimator and the Bayes es...
AbstractIn this paper we propose James–Stein type estimators for variances raised to a fixed power b...
AbstractThe problem of estimating large covariance matrices of multivariate real normal and complex ...
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in h...
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