One of the most important subject in multivariate analysis is parameters estimation. Among
different methods, the shrinkage estimation is of interest. In this paper we consider the …

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 …

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 …

In this paper, we introduce a new shrinkage soft-wavelet threshold estimator based on
Stein's unbiased risk estimate (SURE) for elliptical and spherical distributions 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 …

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 …

Finding the appropriate threshold is one of the most important issues in the wavelet
shrinkage method. Especially when the goal is to estimate the mean matrix parameter for …

In this paper, the generalized Bayes estimator of mean vector parameter for multivariate
normal distribution with Unknown mean vector and covariance matrix is considered. This …