Mena, R.H. and Walker, S.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.
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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.
|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:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Stephen Holland|
|Date Deposited:||19 Dec 2007 19:26|
|Last Modified:||14 Jan 2010 14:05|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/2075 (The current URI for this page, for reference purposes)|
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