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.
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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.
|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)|
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