Minimax estimators in the manova model for arbitrary quadratic loss and unknown covariance matrix

Another Class of Minimax Estimators of A Variance Covariance Matrix in Multivariate Normal Distribution

It is well known that the best equivariant estimator of a variance covari-ance matrix of multivariate normal distribution with respect to the full ane group of transformation is not even minimax. Some minimax estimators have been proposed. Here we treat this problem in the framework of a multivari-ate analysis of variance(MANOVA) model and give other classes of minimax estimators.

the search for the self in becketts theatre: waiting for godot and endgame

this thesis is based upon the works of samuel beckett. one of the greatest writers of contemporary literature. here, i have tried to focus on one of the main themes in becketts works: the search for the real "me" or the real self, which is not only a problem to be solved for beckett man but also for each of us. i have tried to show becketts techniques in approaching this unattainable goal, base...

Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices

In this paper, a shrinkage estimator for the population mean is proposed under known quadratic loss functions with unknown covariance matrices. The new estimator is nonparametric in the sense that it does not assume a specific parametric distribution for the data and it does not require the prior information on the population covariancematrix. Analytical results on the improvement of the propos...

The Beta-MANOVA Ensemble with General Covariance

We find the joint generalized singular value distribution and largest generalized singular value distributions of the β-MANOVA ensemble with positive diagonal covariance, which is general. This has been done for the continuous β > 0 case for identity covariance (in eigenvalue form), and by setting the covariance to I in our model we get another version. For the diagonal covariance case, it has ...

Bayes and Minimax Estimates for Quadratic Loss Functions