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.
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.
|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|
|Deposited By:||Mrs Yaling Zhang|
|Deposited On:||21 Jul 2008 11:58|
|Last Modified:||07 Jan 2015 12:34|
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