@misc{10498/32831,
year = {2024},
url = {http://hdl.handle.net/10498/32831},
abstract = {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.},
keywords = {Multivariate distributions},
keywords = {elliptical symmetry},
keywords = {heavy tails},
keywords = {Bayesian inference},
keywords = {Dirichlet process mixture models},
title = {Bayesian semi-parametric approaches to normal/independent and elliptical distributions},
author = {Sillero-Denamiel, M. Remedios and Marín, Juan Miguel and Ramírez Cobo, Josefa and Ruggeri, Fabrizio and Wiper, Michael P.},
}