Determining and interpreting the order of a two-state Markov Chain: Application to models of daily precipitation

Gregory, JM, Wigley, TML and Jones, PD (1992) Determining and interpreting the order of a two-state Markov Chain: Application to models of daily precipitation. Water Resources Research, 28 (5). p. 1443. ISSN 0043-1397

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Abstract

Lumping together some of the states of a many-state first-order Markov chain does not in general give a first-order Markov chain with a smaller number of states. If a series generated in this way is nevertheless assumed to have been produced by a two-state Markov chain, standard statistical procedures (using the Akaike and Bayesian information criteria) may indicate that it should be fitted by a higher order than first. Stochastic models based on a Markov chain are often used to model precipitation series. It is normal to classify days as "dry' and "wet' and fit a two-state process. In some cases, second- or higher-order chains are preferred by reference to information criteria. This might be because a many-state process, possibly of only first order, would actually be a better choice than a two-state process.

Item Type: Article
Faculty \ School: Faculty of Science > Climatic Research Unit
Faculty of Science > School of Environmental Sciences
Related URLs:
Depositing User: Rosie Cullington
Date Deposited: 14 Jul 2011 14:59
Last Modified: 24 Jul 2019 18:18
URI: https://ueaeprints.uea.ac.uk/id/eprint/33765
DOI: 10.1029/92WR00477

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