Stramer, O. and Roberts, Gareth O. (2007) On Bayesian analysis of nonlinear continuous-time autoregression models. Journal of Time Series Analysis, 28 (5). pp. 744-762.
Full text not available from this repository.Abstract
This article introduces a method for performing fully Bayesian inference for nonlinear conditional autoregressive continuous-time models, based on a finite skeleton of observations. Our approach uses Markov chain Monte Carlo and involves imputing data from times at which observations are not made. It uses a reparameterization technique for the missing data, and because of the non-Markovian nature of the models, it is necessary to adopt an overlapping blocks scheme for sequentially updating segments of missing data. We illustrate the methodology using both simulated data and a data set from the S & P 500 index.
| Item Type: | Article |
|---|---|
| Journal or Publication Title: | Journal of Time Series Analysis |
| Uncontrolled Keywords: | Continuous-time autoregression • Markov chain Monte Carlo • non-linear models |
| Subjects: | Q Science > QA Mathematics |
| Departments: | Faculty of Science and Technology > Lancaster Environment Centre |
| ID Code: | 10035 |
| Deposited By: | Mrs Yaling Zhang |
| Deposited On: | 21 Jul 2008 11:58 |
| Refereed?: | Yes |
| Published?: | Published |
| Last Modified: | 26 Jul 2012 14:42 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/10035 |
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