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Can a Bivariate AR(1) Process Model the Variability of the Inflow into Stochastic Reservoirs?
Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science. statistik.
Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science. statistik.
2006 (English)In: Proceeding (556) Environmental Modelling and Simulation - 2006, 2006Conference paper, Published paper (Refereed)
Abstract [en]

Here we investigate the statistical evidence for a novel class of constrained bivariate AR(1) processes to capture the temporal dynamics of the maximum and minimum for the hourly/daily inflow into a stochastic reservoir. The estima tion is done using a perturbed maximum likelihood tech nique and is illustrated by an empirical example

Place, publisher, year, edition, pages
2006.
Keyword [en]
Hydrological models; autoregression; non-Gaussian time series; range modeling.
Identifiers
URN: urn:nbn:se:uu:diva-25671OAI: oai:DiVA.org:uu-25671DiVA: diva2:53445
Available from: 2007-02-13 Created: 2007-02-13

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Eriksson, AndersPreve, Daniel

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