In this paper, the problem of estimating the mean vector under non-negative constraints on
location vector of the multivariate normal distribution is investigated. The value of the …

Meta-analysis is an indispensable tool for synthesizing statistical results obtained from
individual studies. Recently, non-Bayesian estimators for individual means were proposed …

< jats: p> This paper addresses the problem of Bayesian wavelet estimating the mean vector
of multivariate normal distribution under a multivariate normal prior distribution based on …

In this paper, we consider the generalized Bayes estimator of mean vector parameter for
multivariate normal distribution with unknown mean vector and covariance matrix under …

The estimation of mean vector parameters is very important in elliptical and spherically
models. Among different methods, the Bayesian and shrinkage estimation are interesting. In …

One of the most important issues in matrix-variate normal distribution is the mean matrix
parameter estimation problem. In this paper, we introduce a new soft-threshold wavelet …

Location parameter estimation is an important problem in the point estimation for
multivariate distribution. In this paper, for an elliptical family of distributions with unknown …

This work consists of developing shrinkage estimation strategies for the multivariate normal
mean when the covariance matrix is diagonal and known. The domination of the positive …

Suppose that the random matrix\({\textbf {X}} _ {p\times m}\) has a matrix variate normal
distribution with the mean matrix\(\varvec {\Theta}\) and covariance matrix\({{\varvec …

The matrix-variate normal distribution is a probability distribution that is a generalization of
the multivariate normal distribution to matrix-valued random variables. In this paper, we …