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 1520-6149Full text not available from this repository.
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.
|Uncontrolled Keywords:||α-stable autoregressive process conditionally Gaussian Markov chain Monte Carlo Poisson sum series representation residual approximation|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:49|
|Last Modified:||26 Jan 2015 03:07|