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Locally optimal designs for errors-in-variables models

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Design and Analysis of Experiments

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 where the latter leads to non-concave optimisation problems. 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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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