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dc.contributor.authorTonkin, Matthew
dc.contributor.authorDoherty, John
dc.contributor.authorMoore, Catherine
dc.date.accessioned2010-05-17T23:26:51Z
dc.date.available2010-05-17T23:26:51Z
dc.date.issued2007-07
dc.identifier.citationTonkin, M., Doherty, J., & Moore, C. (2007). Efficient nonlinear predictive error variance for highly parameterized models. Water Resources Research, 43(W07429).en
dc.identifier.issn0043-1397
dc.identifier.urihttps://hdl.handle.net/10182/1879
dc.description.abstractPredictive error variance analysis attempts to determine how wrong predictions made by a calibrated model may be. Predictive error variance analysis is usually undertaken following calibration using a small number of parameters defined through a priori parsimony. In contrast, we introduce a method for investigating the potential error in predictions made by highly parameterized models calibrated using regularized inversion. Vecchia and Cooley (1987) describe a method of predictive error variance analysis that is constrained by calibration data. We extend this approach to include constraints on parameters that lie within the calibration null space. These constraints are determined by dividing parameter space into combinations of parameters for which estimates can be obtained and those for which they cannot. This enables the contribution to predictive error variance from parameterization simplifications required to solve the inverse problem to be quantified, in addition to the contribution from measurement noise. We also describe a novel technique that restricts the analysis to a strategically defined predictive solution subspace, enabling an approximate predictive error variance analysis to be completed efficiently. The method is illustrated using a synthetic and a real-world groundwater flow and transport model.en
dc.language.isoenen
dc.publisherAmerican Geophysical Unionen
dc.relationOriginally published online by the American Geophysical Union.en
dc.relation.urihttps://doi.org/10.1029/2006WR005348en
dc.rightsCopyright 2007 by the American Geophysical Union.en
dc.subjectcalibrationen
dc.subjectpredictive erroren
dc.subjectuncertaintyen
dc.subjectsuperparameteren
dc.titleEfficient nonlinear predictive error variance for highly parameterized modelsen
dc.typeJournal Articleen
lu.contributor.unitLincoln Venturesen
dc.identifier.doi10.1029/2006WR005348
dc.subject.anzsrc0406 Physical Geography and Environmental Geoscienceen
dc.subject.anzsrc090702 Environmental Engineering Modellingen


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