RT journal article
T1 Bayesian semi-parametric approaches to normal/independent and elliptical distributions
A1 Sillero-Denamiel, M. Remedios
A1 Marín, Juan Miguel
A1 Ramírez Cobo, Josefa
A1 Ruggeri, Fabrizio
A1 Wiper, Michael P.
A2 Estadística e Investigación Operativa
K1 Multivariate distributions
K1 elliptical symmetry
K1 heavy tails
K1 Bayesian inference
K1 Dirichlet process mixture models
AB This article introduces new Bayesian, semi-parametric approaches to inference for the general class of elliptical distributions, which includes the 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. Posterior computation is carried out using a Metropolis within Gibbs algorithm. A feature of the method is that the partial lack of identifiability inherent in elliptical distributions can be accounted for by incorporating a restriction on the diagonal elements of the scale matrix. 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, the approach is illustrated with a real data set and a regression example.
YR 2024
FD 2024
LK http://hdl.handle.net/10498/32831
UL http://hdl.handle.net/10498/32831
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026