Date: 2016-03-18

Time: 15:30-16:30

Location: BURN 1205

Abstract:

We consider the problem of estimating the mean vector, q, of a multivariate spherically symmetric distribution under a loss function which is a concave function of squared error. In particular we find conditions on the shrinkage factor under which Stein-type shrinkage estimators dominate the usual minimax best equivariant estimator. In problems where the scale is known, minimax shrinkage factors which generally depend on both the loss and the sampling distribution are found. When the scale is estimated through the squared norm of a residual vector, for a large subclass of concave losses, we find minimax shrinkage factors which are independent of both the loss and the underlying distribution. Recent applications in predictive density estimation are examples where such losses arise naturally.

Speaker

William E. Strawderman is Professor in the Department of Statistics at Rutgers University, Piscataway, NJ.