Mena, R.H. and Walker, S.G. (2005) Stationary autoregressive models via a Bayesian nonparametric approach. Journal of Time Series Analysis, 26 (6). pp. 789-805. ISSN 0143-9782.
| The full text of this publication is not available from this repository. (Contact us about this Publication) | |
| 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 |
|---|---|
| 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: | Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics |
| Depositing User: | Judith Broom |
| Date Deposited: | 11 Sep 2008 09:13 |
| Last Modified: | 14 Jan 2010 14:40 |
| Resource URI: | http://kar.kent.ac.uk/id/eprint/10533 (The current URI for this page, for reference purposes) |
- Depositors only (login required):
