On Bayesian Robustness: An Asymptotic Approach

  • Daniel Peña
  • Ruben H. Zamar
Part of the Lecture Notes in Statistics book series (LNS, volume 109)


This paper presents a new asymptotic approach to study the robustness of Bayesian inference to changes on the prior distribution. We study the robustness of the posterior density score function when the uncertainty about the prior distribution has been restated as a problem of uncertainty about the model parametrization. Classical robustness tools, such as the influence function and the maximum bias function, are defined for uniparametric models and calculated for the location case. Possible extensions to other models are also briefly discussed.

Key words and phrases

gross error sensitivity influence function maximum bias curve prior robustness 

AMS 1980 subject classifications

Primary 62F15 secondary 62F35 


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Copyright information

© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • Daniel Peña
    • 1
  • Ruben H. Zamar
    • 2
  1. 1.Universidad Carlos III de MadridSpain
  2. 2.University of British ColumbiaVancouverCanada

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