Robust Bayesian methodology deals with the problem of explaining uncertainty of the inputs (the prior, the model, and the loss function) and provides a breakthrough way to take into account the input’s variation. If the uncertainty is in terms of the prior knowledge, robust Bayesian analysis...

For the p-variate Poisson mean, under the sum of weighted squared error losses, weights being reciprocals of variances, a class of proper Bayes minimax estimates dominating the usual estimate, namely the sample mean is produced. An example is given to illustrate this. The interrelation of our...

For estimating an unknown parameter [theta], we introduce and motivate the use of the balanced-type loss function: , where 0[less-than-or-equals, slant][omega][less-than-or-equals, slant]1, q([theta]) is a positive weight function, and [delta]0 is a general "target" estimator. Developments and...

For a vast class of discrete model families with cdf's F[theta], and for estimating [theta] under squared error loss under a constraint of the type [theta][set membership, variant][0,m], we present a general and unified development concerning the minimaxity of a boundary supported prior Bayes...