Likelihood analysis of non-Gaussian measurement time series
- 1 September 1997
- journal article
- research article
- Published by Oxford University Press (OUP) in Biometrika
- Vol. 84 (3) , 653-667
- https://doi.org/10.1093/biomet/84.3.653
Abstract
In this paper we provide methods for estimating non-Gaussian time series models. These techniques rely on Markov chain Monte Carlo to carry out simulation smoothing and Bayesian posterior analysis of parameters, and on importance sampling to estimate the likelihood function for classical inference. The time series structure of the models is used to ensure that our simulation algorithms are efficient.Keywords
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