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Locally optimal designs for errors-in-variables models

Wednesday 8th July 2015 - 09:40 to 10:20
INI Seminar Room 1
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.
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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons