We derive minimax generalized Bayes estimators of regression coefficients in the general linear model with spherically symmetric errors under invariant quadratic loss for the case of unknown scale. The class of estimators generalizes the class considered in Maruyama and Strawderman [Y. Maruyama,...

Summary The problem of estimating a mean vector for spherically symmetric distributions with the quadratic loss function is considered. A robust generalized Bayes estimator improving on the James-Stein estimator is given.

Averaged orthogonal rotations of Zellner's g-prior yield general, interpretable, closed form Bayes factors for the normal linear model variable selection problem. Coupled with a model space prior that balances the weight between the identifiable and the unidentifiable models, limiting forms for...

A sufficient condition for the admissibility of generalized Bayes estimators of the location vector of spherically symmetric distribution under squared error loss is derived. This is as strong a condition as that of Brown [L.D. Brown, Admissible estimators, recurrent diffusions, and insoluble...

The problem of estimating the quadratic loss function for the estimator of a multivariate normal mean is considered. A positive estimator which dominates Johnstone (1987)'s shrinkage rule is given.