Bayesian Inference for Mixture of Stable Distributions
Roberto Casarin · ARCA (Università Ca' Foscari Venezia) · 2003
In many different fields such as hydrology, telecommunications, physics of condensed matter and finance, the gaussian model results unsatisfactory and reveals difficulties in fitting data with skewness, heavy tails and multimodality.The use of stable distributions allows for modelling skewness and heavy tails but gives rise to inferential problems related to the estimation of the stable distribution's parameters.The aim of this work is to generalise the stable distribution framework by introducing a model that accounts also for multimodality.In particular we introduce a stable mixture model and a suitable reparameterisation of the mixture, which allow us to make inference on the mixture parameters.We use a full Bayesian approach and MCMC simulation techniques for the estimation of the posterior distribution.