%0 Journal Article 
%A Sillero-Denamiel, M. Remedios
%A Marín, Juan Miguel
%A Ramírez Cobo, Josefa
%A Ruggeri, Fabrizio
%A Wiper, Michael P.
%T Bayesian semi-parametric approaches to normal/independent and elliptical distributions
%D 2024
%U http://hdl.handle.net/10498/32831
%X 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.
%K Multivariate distributions
%K elliptical symmetry
%K heavy tails
%K Bayesian inference
%K Dirichlet process mixture models
%~ Universidad de Cádiz