Bayesian Robustness with Quantile Loss Functions
نویسندگان
چکیده
Bayes decision problems require subjective elicitation of the inputs: beliefs and preferences. Sometimes, elicitation methods may not perfectly represent the Decision Maker’s judgements. Several foundations propose to overlay this problem using robust approaches. In these models, beliefs are modelled by a class of probability distributions and preferences by a class of loss functions. Thus, the solution concept is the set of non-dominated alternatives. In this paper we focus on the computation of the efficient set when the preferences are modelled by a class of convex loss functions, specifically the quantile loss functions. We illustrate the idea with examples and introduce the use of stochastic dominance in this context.
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