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New classes of priors based on stochastic orders and distortion functions

Academic Article
Publication Date:
2016
abstract:
In the context of robust Bayesian analysis, we introduce a new class of prior distributions based on stochastic orders and distortion functions. We provide the new definition, its interpretation and the main properties and we also study the relationship with other classical classes of prior beliefs. We also consider Kolmogorov and Kantorovich metrics to measure the uncertainty induced by such a class, as well as its effect on the set of corresponding Bayes actions. Finally, we conclude the paper with some numerical examples.
Iris type:
01.01 Articolo in rivista
Keywords:
robust Bayesian analysis; Bayesian sensitivity; class of priors; stochastic orders; distortion functions
List of contributors:
Ruggeri, Fabrizio
Handle:
https://iris.cnr.it/handle/20.500.14243/328027
Published in:
BAYESIAN ANALYSIS
Journal
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URL

http://projecteuclid.org/euclid.ba/1448590531
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