Bayesian analysis of multivariate stable distributions using one-dimensional projections

Abstract

In this paper we take up Bayesian inference in general multivariate stable distributions. We exploit the representation of Matsui and Takemura (2009) for univariate projections, and the representation of the distributions in terms of their spectral measure. We present efficient MCMC schemes to perform the computations when the spectral measure is approximated discretely or, as we propose, by a normal distribution. Appropriate latent variables are introduced to implement MCMC. In relation to the discrete approximation, we propose efficient computational schemes based on the characteristic function.

References in corpus (2)

• One-Step R-Estimation in Linear Models with Stable Errors
• Integral representations of one dimensional projections for multivariate stable densities