Stationary mixture transition distribution (MTD) models via predictive distributions

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. (The full text of this publication is not available from this repository)

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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
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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