, Volume 5, Issue 2, pp 395–409 | Cite as

Bayesian robustness on constrained density band classes

  • M. Perone-Pacifico
  • G. Salinetti
  • L. Tardella


An interesting class in Bayesian robustness is a ‘band’ of priors: its flexibility allows for different tail behaviours while excluding point masses. In this paper, we consider density band classes of priors with additional constraints modelling different available prior information: quantiles, moments, constraints derived from the probability of observables or from the dependence structure in a multidimensional setting. The proposed techniques allow us to obtain the range of quantities of interest that are not linear or ratio linear functionals. Numerical examples are provided.


Bayesian robustness Density band class Neyman and pearson lemma Linear constrained optimization Fractional optimization 


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Copyright information

© SEIO 1996

Authors and Affiliations

  • M. Perone-Pacifico
    • 1
  • G. Salinetti
    • 1
  • L. Tardella
    • 1
  1. 1.Dip. di Statistica, Probabilità e Statistiche ApplicateUniv. di Roma “La Sapienza”Roma

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