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On Bayesian analysis of nonlinear continuous-time autoregression models.

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.

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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: 17 Sep 2013 08:15
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/10035

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