Mena, Ramses H., Walker, Stephen G. (2005) Stationary autoregressive models via a Bayesian nonparametric approach. Journal of Time Series Analysis, 26 (6). pp. 789-805. ISSN 0143-9782. (doi:10.1111/j.1467-9892.2005.00429.x) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:10533)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: http://dx.doi.org/10.1111/j.1467-9892.2005.00429.x |
Abstract
An approach to constructing strictly stationary AR(1)-type models with arbitrary stationary distributions and a flexible dependence structure is introduced. Bayesian nonparametric predictive density functions, based on single observations, are used to construct the one-step ahead predictive density. This is a natural and highly flexible way to model a one-step predictive/transition density.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1111/j.1467-9892.2005.00429.x |
| Uncontrolled keywords: | AR model; Bayesian nonparametrics; random probability measure; stationary process TIME-SERIES MODELS; DENSITY-ESTIMATION; DISTRIBUTIONS; SEQUENCES; MIXTURES; MARGINS |
| Subjects: | Q Science > QA Mathematics (inc Computing science) |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
| Depositing User: | Judith Broom |
| Date Deposited: | 11 Sep 2008 09:13 UTC |
| Last Modified: | 16 Nov 2021 09:49 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/10533 (The current URI for this page, for reference purposes) |
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