AbstractTn the normal case it is well known that, although the James-Stein rule is minimax, it is not admissible and the associated positive rule is one way to improve on it. We extend this result to the class of the spherically symmetric distributions and to a large class of shrinkage rules. Moreover we propose a family of generalized positive rules. We compare our results to those of Berger and Bock (Statistical Decision Theory and Related Topics, II, Academic Press, New York. 1976). In particular our conditions on the shrinkage estimator are weaker
In estimation of ratio of variances in two normal distributions with unknown means, it has been show...
AbstractWhen estimating, under quadratic loss, the location parameterθof a spherically symmetric dis...
In estimation of ratio of variances in two normal distributions with unknown means, it has been show...
AbstractTn the normal case it is well known that, although the James-Stein rule is minimax, it is no...
AbstractThis paper is primarily concerned with extending the results of Brandwein and Strawderman in...
This book provides a coherent framework for understanding shrinkage estimation in statistics. The te...
Consider the problem of estimating the mean vector [theta] of a random variable X in , with a spheri...
This paper addresses the problem of estimating the mean matrix of an elliptically contoured distribu...
AbstractConsider the problem of estimating the mean vector θ of a random variable X in Rp, with a sp...
In this paper, we are interested in estimating a multivariate normal mean under the balanced loss fu...
16 pages, 1 article*Shrinkage Estimators Under Spherically Symmetry for the General Linear Model wit...
The dissertation considers three different topics which pertain to minimax shrinkage estimation: 1)...
AbstractThe estimation of the location parameter of a spherically symmetric distribution was greatly...
The subject of this master thesis is shrinkage estimators for the location parameter of an elliptica...
The subject of this master thesis is shrinkage estimators for the location parameter of an elliptica...
In estimation of ratio of variances in two normal distributions with unknown means, it has been show...
AbstractWhen estimating, under quadratic loss, the location parameterθof a spherically symmetric dis...
In estimation of ratio of variances in two normal distributions with unknown means, it has been show...
AbstractTn the normal case it is well known that, although the James-Stein rule is minimax, it is no...
AbstractThis paper is primarily concerned with extending the results of Brandwein and Strawderman in...
This book provides a coherent framework for understanding shrinkage estimation in statistics. The te...
Consider the problem of estimating the mean vector [theta] of a random variable X in , with a spheri...
This paper addresses the problem of estimating the mean matrix of an elliptically contoured distribu...
AbstractConsider the problem of estimating the mean vector θ of a random variable X in Rp, with a sp...
In this paper, we are interested in estimating a multivariate normal mean under the balanced loss fu...
16 pages, 1 article*Shrinkage Estimators Under Spherically Symmetry for the General Linear Model wit...
The dissertation considers three different topics which pertain to minimax shrinkage estimation: 1)...
AbstractThe estimation of the location parameter of a spherically symmetric distribution was greatly...
The subject of this master thesis is shrinkage estimators for the location parameter of an elliptica...
The subject of this master thesis is shrinkage estimators for the location parameter of an elliptica...
In estimation of ratio of variances in two normal distributions with unknown means, it has been show...
AbstractWhen estimating, under quadratic loss, the location parameterθof a spherically symmetric dis...
In estimation of ratio of variances in two normal distributions with unknown means, it has been show...