Bayesian semi-parametric approaches to normal/independent and elliptical distributions

M. Remedios Sillero-Denamiel, J. Miguel Marín, Pepa Ramírez-Cobo, Fabrizio Ruggeri and Michael P. Wiper

Journal of Applied Statistics, 2026, vol. 53, issue 6, 1130-1157

Abstract: This article introduces a novel, Bayesian, semi-parametric approach to inference for both elliptical and normal/independent distributions. The location and scale parameters are modelled parametrically and a suitable transformation of the modular variable is modelled using Dirichlet process mixtures. A feature of our approach is that the partial lack of identifiability inherent in both elliptical and normal/independent distributions can be accounted for by incorporating a restriction on the diagonal elements of the scale matrix. Posterior computation is carried out using a Markov chain Monte Carlo algorithm.A novel technique for model selection, based on an approximation of the deviation information criterion, is introduced. As shown by a numerical study based on simulation, the approach can be used to discriminate between elliptical, and normal/independent distributions. Finally, our methodology is illustrated with both simulated and real data.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2025.2552728 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:53:y:2026:i:6:p:1130-1157

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2025.2552728

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().