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

Gregory, JM, Wigley, TML and Jones, PD ORCID: https://orcid.org/0000-0001-5032-5493 (1992) Determining and interpreting the order of a two-state Markov Chain: Application to models of daily precipitation. Water Resources Research, 28 (5). pp. 1443-1446. 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 > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups:	Faculty of Science > Research Groups > Climatic Research Unit Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User:	Rosie Cullington
Date Deposited:	14 Jul 2011 14:59
Last Modified:	16 Jun 2023 23:57
URI:	https://ueaeprints.uea.ac.uk/id/eprint/33765
DOI:	10.1029/92WR00477

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