Mena, Ramses H., Walker, Stephen G. (2007) Stationary mixture transition distribution (MTD) models via predictive distributions. Journal of Statistical Planning and Inference, 137 (10). pp. 3103-3112. ISSN 0378-3758. (doi:10.1016/j.jspi.2006.05.018) (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:2075)
| 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/10.1016/j.jspi.2006.05.018 |
Abstract
This paper combines two ideas to construct autoregressive processes of arbitrary order. The first idea is the construction of first order stationary processes described in Pitt et al. [(2002). Constructing first order autoregressive models via latent processes. Scand. J. Statist. 29, 657-663] and the second idea is the construction of higher order processes described in Raftery [(1985). A model for high order Markov chains. J. Roy Statist. Soc. B. 47, 528-539]. The resulting models provide appealing alternatives to model non-linear and non-Gaussian time series.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1016/j.jspi.2006.05.018 |
| Additional information: | Special issue |
| Uncontrolled keywords: | AR model; Bayesian non-parametrics; MTD models; random probability measure; stationary process |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
| Depositing User: | Stephen Holland |
| Date Deposited: | 19 Dec 2007 19:26 UTC |
| Last Modified: | 16 Nov 2021 09:40 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/2075 (The current URI for this page, for reference purposes) |
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