Posterior regret Γ-minimax estimation in a normal model with asymmetric loss function

On Γ-minimax Estimation with Ordered Observations

In this note we derive Γ-minimax estimators for the location parameter of the normal and uniform models. All distributions with uniformly bounded variances and means belonging to a fixed interval form a class of priors, Γ. We restrict ourselves to the rules which are linear combinations of order statistics. Optimal decision rules in such decisiontheoretic framework are, as expected, rules linea...

BAYES ESTIMATION USING A LINEX LOSS FUNCTION

This paper considers estimation of normal mean ? when the variance is unknown, using the LINEX loss function. The unique Bayes estimate of ? is obtained when the precision parameter has an Inverse Gaussian prior density

Estimation of the Multivariate Normal Mean under the Extended Reflected Normal Loss Function

Statistical Treatment Choice Based on Asymmetric Minimax Regret Criteria

This paper studies the problem of treatment choice between a status quo treatment with a known outcome distribution and an innovation whose outcomes are observed only in a representative finite sample. I evaluate statistical decision rules, which are functions that map sample outcomes into the planner’s treatment choice for the population, based on regret, which is the expected welfare loss due...

Bayesian Estimation for the Pareto Income Distribution under Asymmetric LINEX Loss Function

The use of the Pareto distribution as a model for various socio-economic phenomena dates back to the late nineteenth century. In this paper, after some necessary preliminary results we deal with Bayes estimation of some of the parameters of interest under an asymmetric LINEX loss function, using suitable choice of priors when the scale parameter is known and unknown. Results of a Monte C...