Hu, Rui; Wiens, Douglas P. Robust discrimination designs over Hellinger neighbourhoods. (English) Zbl 1378.62036 Ann. Stat. 45, No. 4, 1638-1663 (2017). 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. Reviewer: Fabio Rapallo (Alessandria) Cited in 1 Document MSC: 62K99 Design of statistical experiments 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62J02 General nonlinear regression 62K25 Robust parameter designs Keywords:adaptive design; Hellinger distance; Kullback-Leibler divergence; maximin; Michaelis-Menten model; Neyman-Pearson test; nonlinear regression; optimal design; robustness; sequential design PDFBibTeX XMLCite \textit{R. Hu} and \textit{D. P. Wiens}, Ann. Stat. 45, No. 4, 1638--1663 (2017; Zbl 1378.62036) Full Text: DOI Euclid