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A nonparametric Markov Model for daily river flow
Author(s) -
Yakowitz Sidney J.
Publication year - 1979
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr015i005p01035
Subject(s) - markov chain , nonparametric statistics , autoregressive model , class (philosophy) , markov model , computer science , convergence (economics) , statistical model , parametric statistics , streamflow , markov process , econometrics , mathematics , statistics , artificial intelligence , geography , machine learning , cartography , economics , drainage basin , economic growth
This paper presents to an audience of research hydrologists what is believed to be a significant new development in time series modeling. The model class is the class of (not necessarily finite state) Markov chains. The basic advantage of this class is that in comparison to parametric models (such as autoregressive moving average) it is a very rich class, and the value of the statistical method described herein is that, as proven elsewhere, it provides convergence over this large class. The technique is applied to Cheyenne River data, and discussion is provided on how to incorporate prior statistical and geological information into the model. Also, comparisons are made between the nonparametric Markov analysis provided here and the currently popular streamflow models and statistical techniques.