We consider minimax shrinkage estimation of a location vector of a spherically symmetric distribution under a loss function which is a concave function of the usual squared error loss. In particular for distributions which are scale mixtures of normals (and somewhat more generally), and for...

Our investigation concerns the estimation of predictive densities and a study of effiency as measured by the frequentist risk of such predictive densities with integrated L2 and L1 losses. Our findings relate to a p-variate spherically symmetric observable X ∼ px (||x -μ||2) and the...

This paper is concerned with estimation of a predictive density with parametric constraints under Kullback-Leibler loss. When an invariance structure is embed- ded in the problem, general and unied conditions for the minimaxity of the best equivariant predictive density estimator are derived....

We consider minimax shrinkage estimation of location for spherically symmetric distributions under a concave function of the usual squared error loss. Scale mixtures of normal distributions and losses with completely monotone derivatives are featured.

For estimating a bounded normal mean with known variance, we exhibit situations of pronounced discrepancy between the credibility of Bayes credible regions and frequentist coverage. Analogously, frequentist confidence intervals are shown to have credibility one in some cases.

For a vast array of general spherically symmetric location-scale models with a residual vector, we consider estimating the (univariate) location parameter when it is lower bounded. We provide conditions for estimators to dominate the benchmark minimax MRE estimator, and thus be minimax under...

We introduce a family of bivariate discrete distributions whose members are generated by a decreasing mass function <italic>p</italic>, and with margins given by <italic>p</italic>. Several properties and examples are obtained, including a family of seemingly novel bivariate Poisson distributions.

This paper is concerned with estimation of a predictive density with parametric constraints under Kullback–Leibler loss. When an invariance structure is embedded in the problem, general and unified conditions for the minimaxity of the best equivariant predictive density estimator are derived....

For independently distributed observables: Xi~N([theta]i,[sigma]2),i=1,...,p, we consider estimating the vector [theta]=([theta]1,...,[theta]p)' with loss ||d-[theta]||2 under the constraint , with known [tau]1,...,[tau]p,[sigma]2,m. In comparing the risk performance of Bayesian estimators...