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.
Original language | English |
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Pages (from-to) | 1443-1446 |
Number of pages | 4 |
Journal | Water Resources Research |
Volume | 28 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1992 |