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Project C2: Optimal Design of Experiments for Dynamic Processes

Project leaders

Holger Dette

Joachim Kunert





The project proposes optimal designs for experiments in the context of dynamic models. It extends conventional theory for regression models with correlated observations to more general error processes, non-linear models and multivariate settings (time-space dependencies). Major topics are optimal design for the minimization of the mean square error, for model selection and for inverse problems. Another goal is the optimization of the design under model uncertainty, i.e. the construction of efficient designs for model selection, which require less a priori knowledge about the covariance structure and the specification of parameter values.