Konstantinou, MariaDette, Holger2014-09-052014-09-052014-09-05http://hdl.handle.net/2003/3361110.17877/DE290R-6876This 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.enDiscussion Paper / SFB 823;31/2014error-in-variable modelD-optimalitynonlinear regressionoptimal design310330620working paper