Authors: Konstantinou, Maria
Dette, Holger
Title: Locally optimal designs for errors-in-variables models
Language (ISO): en
Abstract: This paper considers the construction of optimal designs for nonlinear regres- sion models when there are measurement errors in the predictor. Corresponding (approximate) design theory is developed for maximum likelihood and least squares estimation, where the latter leads to non-concave optimisation problems. For the Michaelis-Menten, EMAX and exponential regression model D-optimal designs can be found explicitly and compared with the corresponding designs derived under the assumption of no measurement error in concrete applications.
Subject Headings: error-in-variable model
nonlinear regression
optimal design
Issue Date: 2014-09-05
Appears in Collections:Sonderforschungsbereich (SFB) 823

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