Stationary autoregressive models via a Bayesian nonparametric approach

Mena, Ramses H. and 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. (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)

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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: 25 Jun 2014 10:45
Resource URI: https://kar.kent.ac.uk/id/eprint/10533 (The current URI for this page, for reference purposes)
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