Robust optimum designs to a misspecified model

Usually, in the Theory of Optimal Experimental Design the model is assumed to be known at the design stage. In practice, however, more competing models may be plausible for the same data. Thus, a possibility is to find an optimal design which take both model discrimination and parameter estimation into consideration. In this paper we follow a different approach: we find a design which is optimum for estimation purposes but is also robust to a misspecified model. In other words, the optimum design is "good" for estimating the unknown parameters even if the assumed model is not correct