Locally optimal designs for errors-in-variables models Seminar

We consider the construction of locally optimal designs for nonlinear regression models when there are measurement errors in the predictors. Corresponding (approximate) design theory is developed for models subject to a functional error structure for both maximum likelihood and least squares estimation. Locally D-optimal designs are found explicitly for the Michaelis-Menten, EMAX and exponential regression models and are then compared with the corresponding designs derived under the assumption of no measurement error in concrete applications.