Dominant parameter selection in the marginally identifiable case

Ilya Ioslovich, Per-Olof Gutman and Ido Seginer

Mathematics and Computers in Simulation (MATCOM), 2004, vol. 65, issue 1, 127-136

Abstract: Often a rather limited set of experimental data is available for the identification of a dynamic model, which contains many parameters. This is, e.g. the usual case for crop growth models. In this situation, only some parameter values can be estimated. Based on an analysis of the Fisher information matrix, a method for a reasonable selection of parameters is suggested here. The method chooses the most sensitive parameters, i.e. those to which the model under the considered experimental conditions is most sensitive, and excludes both coupled parameters and those that exhibit multiplecorrelation. A comparison with different ridge regression methods is made. The methodology is illustrated with a simple lettuce growth model.

Keywords: Model calibration; System identification; Crop growth models; Fisher matrix (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:65:y:2004:i:1:p:127-136

DOI: 10.1016/j.matcom.2003.09.012

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