Bayesian inference for exponential mixtures is presented in the paper, including the choice of a non-informative prior based on a location-scale reparametrization of the mixture. Adapted control sheets are proposed for studying the convergence of the associated Gibbs sampler. They exhibit a...

The estimation of quadratic functions of a multivariate normal mean is an inferential problem which, while being simple to state and often encountered in practice, leads to surprising complications both from frequentist and Bayesian points of view. The drawbacks of Bayesian inference using the...

This paper develops an extension of the Riemann sum techniques of Philippe (1997b) in the setup of MCMCC algorithms. It shows that the technique applies equally well to the output of these algorithms, with similar speeds of convergence which improve upon the regular estimator. The restriction on...

Tn 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...

In this paper we consider the problem of estimating the quadratic loss of point estimators of a location parameter for a family of spherically symmetric distributions. We compare the unbiased loss estimator of the minimax estimator with a new shrinkage type loss estimator. Conditions on the...

This paper is primarily concerned with extending the results of Brandwein and Strawderman in the usual canonical setting of a general linear model when sampling from a spherically symmetric distribution. When the location parameter belongs to a proper linear subspace of the sampling space, we...