Application of objective priors for the multivariate Lomax distribution

Sang Gil Kang, Woo Dong Lee and Yongku Kim

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 7, 2307-2328

Abstract: For a model incorporating the effect of a common environment on several components of a system, a multivariate Lomax distribution (MLD) is generally considered by mixing exponential variables. Objective Bayesian has very good frequentist properties and provides a moderate solution for the prior elicitation which is one of important and difficult issues on Bayesian analysis. In this paper, we develop noninformative priors, such as the probability matching priors and reference priors, for the parameters of the MLD. We proved that a reference prior for the shape parameter is a first-order probability matching prior, but the reference priors for the scale parameters do not satisfy the first-order matching criterion. In addition, a second-order probability matching prior does not exist for all parameters. We also presented the conditions that make the posterior distributions for the general prior, including the probability matching prior and reference priors, to be proper. In particular, Jeffreys’ prior and probability matching priors for all parameters give proper posteriors, whereas reference priors for scale parameters give improper posteriors.

Date: 2024
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DOI: 10.1080/03610926.2022.2126945

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