Locally optimal designs for errors-in-variables models

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