In the context of robust Bayesian analysis for multiparameter distributions, we introduce a new class of priors based on stochastic orders, multivariate total positivity of order 2 (MTP2) and weighted distributions. 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 the Hellinger metric and the Kullback-Leibler divergence to measure the uncertainty induced by such a class, as well as its effect on the posterior distribution. Finally, we conclude the paper with a real example about train door reliability.

On a new class of multivariate prior distributions: Theory and application in reliability

F Ruggeri;
2021

Abstract

In the context of robust Bayesian analysis for multiparameter distributions, we introduce a new class of priors based on stochastic orders, multivariate total positivity of order 2 (MTP2) and weighted distributions. 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 the Hellinger metric and the Kullback-Leibler divergence to measure the uncertainty induced by such a class, as well as its effect on the posterior distribution. Finally, we conclude the paper with a real example about train door reliability.
2021
Istituto di Matematica Applicata e Tecnologie Informatiche - IMATI -
robust Bayesian analysis
Bayesian sensitivity
class of priors
stochastic orders
multivariate total positivity
weighted distributions
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/404428
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