PLU Robust Bayesian Decision Theory: Point Estimation
提出一种将非稳健密度和效用函数转换为稳健版本的技术，用于贝叶斯点估计，并应用于线性模型的稳健分析。
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.