Skip to main content
Kent Academic Repository

On the construction of stationary AR(1) models via random distributions

Contreras-Cristan, Alberto, Mena, Ramses H., Walker, Stephen G. (2009) On the construction of stationary AR(1) models via random distributions. Statistics, 43 (3). pp. 227-240. ISSN 0233-1888. (doi:10.1080/02331880802259391) (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:12680)

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/10.1080/02331880802259391

Abstract

We explore a method for constructing first-order stationary autoregressive-type models with given marginal distributions. We impose the underlying dependence structure in the model using Bayesian non-parametric predictive distributions. This approach allows for nonlinear dependency and at the same time works for any choice of marginal distribution. In particular, we look at the case of discrete-valued models; that is the marginal distributions are supported on the non-negative integers.

Item Type: Article
DOI/Identification number: 10.1080/02331880802259391
Uncontrolled keywords: AR model; Beta-Stacy process; Bayesian non-parametrics; discrete-valued time series; Plya trees; stationary process
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: 17 Mar 2009 15:44 UTC
Last Modified: 16 Nov 2021 09:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/12680 (The current URI for this page, for reference purposes)

University of Kent Author Information

Walker, Stephen G..

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.