Parameter estimation in enzyme-kinetics with consideration of heteroscedasticity and low dose data

Marcus Brunnert and Frank Gilberg

No 1999,49, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

Abstract: In this paper we propose a simulation study in order to discuss four statistical models dealing with the problem of parameter estimation in enzyme-kinetics. The pseudo-maximumlikelihood estimators for the transform-both-sides-model and the weighted TBS-model are compared with least-square-estimators of the classical nonlinear regression model and the linearized Eadie-Hofstee-plot. Due to heteroscedasticity of enzyme-kinetic data in low dose experiments the proposed estimators are investigated.

Keywords: Nonlinear regression model; Pseudo-maximum-likelihood estimation; Heteroscedastic error variance; Michaelis-Menten-kinetic; Low dose data; Simulation study (search for similar items in EconPapers)
Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:199949

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