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. ISSN 0143-9782

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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:
Journal Article
Journal or Publication Title:
Journal of Time Series Analysis
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
ID Code:
81973
Deposited By:
Deposited On:
08 Oct 2016 00:06
Refereed?:
Yes
Published?:
Published
Last Modified:
20 May 2020 01:13