AbstractWhen estimating, under quadratic loss, the location parameterθof a spherically symmetric distribution with known scale parameter, we show that it may be that the common practice of utilizing the residual vector as an estimate of the variance is preferable to using the known value of the variance. In the context of Stein-like shrinkage estimators, we exhibit sufficient conditions on the spherical distributions for which this paradox occurs. In particular, we show that it occurs fort-distributions when the dimension of the residual vector is sufficiently large. The main tools in the development are upper and lower bounds on the risks of the James–Stein estimators which are exact atθ=0
We consider the estimation of the mean of a multivariate normal distribution with known variance. Mo...
Charles Stein discovered a paradox in 1955 that many statisticians think is of\ud fundamental import...
Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importan...
AbstractThe estimation of the location parameter of a spherically symmetric distribution was greatly...
AbstractWe investigate conditions under which estimators of the form X + aU′Ug(X) dominate X when X,...
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...
Shrinkage estimation has become a basic tool in the analysis of high-dimensional data. Historically ...
Shrinkage estimation has become a basic tool in the analysis of high-dimensional data. Historically ...
AbstractConsider the problem of estimating the mean vector θ of a random variable X in Rp, with a sp...
AbstractIn this paper we propose James–Stein type estimators for variances raised to a fixed power b...
The dissertation considers three different topics which pertain to minimax shrinkage estimation: 1)...
Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importan...
AbstractThe shrinkage effect is studied in estimating the expectation vector by weighting of mean ve...
We consider the estimation of the mean of a multivariate normal distribution with known variance. Mo...
Charles Stein discovered a paradox in 1955 that many statisticians think is of\ud fundamental import...
Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importan...
AbstractThe estimation of the location parameter of a spherically symmetric distribution was greatly...
AbstractWe investigate conditions under which estimators of the form X + aU′Ug(X) dominate X when X,...
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...
Shrinkage estimation has become a basic tool in the analysis of high-dimensional data. Historically ...
Shrinkage estimation has become a basic tool in the analysis of high-dimensional data. Historically ...
AbstractConsider the problem of estimating the mean vector θ of a random variable X in Rp, with a sp...
AbstractIn this paper we propose James–Stein type estimators for variances raised to a fixed power b...
The dissertation considers three different topics which pertain to minimax shrinkage estimation: 1)...
Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importan...
AbstractThe shrinkage effect is studied in estimating the expectation vector by weighting of mean ve...
We consider the estimation of the mean of a multivariate normal distribution with known variance. Mo...
Charles Stein discovered a paradox in 1955 that many statisticians think is of\ud fundamental import...
Charles Stein discovered a paradox in 1955 that many statisticians think is of fundamental importan...