PLU Robust Bayesian Decision Theory: Point Estimation

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Abstract
The development of data analysis techniques that are robust with respect to wild or extreme observations is now a major concern. From a Bayesian point of view, the concept of robustness also pertains to the choice of a prior density (P robustness) and a utility function (U robustness), as well as the likelihood (L robustness). A technique for converting commonly used nonrobust density and utility functions to robust versions is described that provides convenient solutions for point estimates. Applications of this procedure to the robust Bayesian analysis of the linear model are provided.

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