$${\mathcal{L}}_p$$ loss functions: a robust bayesian approach

In bayesian inference, the Bayes estimator is the alternative with the minimum expected loss. In most cases, the loss function shows the distance between the alternative and the parameter. Therefore, any distance can lead to a loss function. Among the best known distance functions is L _p one, where the choice of value p may be difficult and arbitrary. This paper examines robust models where the loss function is modelled by family L _p . Our solution concept is the non-dominated alternative. We characterize the non-dominated set by having the posterior distribution function satisfy a particular asymmetry property. We also include an example to illustrate the methodology described.