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Robust discrimination designs over Hellinger neighbourhoods. (English) Zbl 1378.62036

Authors’ abstract: To aid in the discrimination between two, possibly nonlinear, regression models, we study the construction of experimental designs. Considering that each of these two models might be only approximately specified, robust “maximin” designs are proposed. The rough idea is as follows. We impose neighbourhood structures on each regression response, to describe the uncertainty in the specifications of the true underlying models. We determine the least favourable – in terms of Kullback-Leibler divergence – members of these neighbourhoods. Optimal designs are those maximizing this minimum divergence. Sequential, adaptive approaches to this maximization are studied. Asymptotic optimality is established.

MSC:

62K99 Design of statistical experiments
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62J02 General nonlinear regression
62K25 Robust parameter designs
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