Theory and Decision

, Volume 48, Issue 4, pp 359–381

Global Robustness with Respect to the Loss Function and the Prior

Authors

  • Christophe Abraham
    • Unité de BiométrieENSA.M INRA
  • Jean-Pierre Daures
    • Unité de BiométrieENSA.M INRA
Article

DOI: 10.1023/A:1005212125699

Cite this article as:
Abraham, C. & Daures, J. Theory and Decision (2000) 48: 359. doi:10.1023/A:1005212125699

Abstract

We propose a class [I,S] of loss functions for modeling the imprecise preferences of the decision maker in Bayesian Decision Theory. This class is built upon two extreme loss functions I and S which reflect the limited information about the loss function. We give an approximation of the set of Bayes actions for every loss function in [I,S] and every prior in a mixture class; if the decision space is a subset of ℝ, we obtain the exact set.

Bayesian Decision Theory Global robustness Loss function Mixture class

Copyright information

© Kluwer Academic Publishers 2000