CUED Publications database

Linear gaussian computations for near-exact Bayesian Monte Carlo inference in skewed alpha-stable time series models

Lemke, T and Godsill, SJ (2012) Linear gaussian computations for near-exact Bayesian Monte Carlo inference in skewed alpha-stable time series models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 3737-3740. ISSN 1520-6149

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Abstract

In this paper we study parameter estimation for time series with asymmetric α-stable innovations. The proposed methods use a Poisson sum series representation (PSSR) for the asymmetric α-stable noise to express the process in a conditionally Gaussian framework. That allows us to implement Bayesian parameter estimation using Markov chain Monte Carlo (MCMC) methods. We further enhance the series representation by introducing a novel approximation of the series residual terms in which we are able to characterise the mean and variance of the approximation. Simulations illustrate the proposed framework applied to linear time series, estimating the model parameter values and model order P for an autoregressive (AR(P)) model driven by asymmetric α-stable innovations. © 2012 IEEE.

Item Type: Article
Uncontrolled Keywords: α-stable autoregressive process conditionally Gaussian Markov chain Monte Carlo Poisson sum series representation residual approximation
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:49
Last Modified: 10 Dec 2014 08:45
DOI: 10.1109/ICASSP.2012.6288729