Robust discrimination designs over Hellinger neighbourhoods
针对两个可能近似指定的非线性回归模型，研究如何构造实验设计以稳健地判别它们，提出最大化最小Kullback-Leibler散度的极大极小设计，并建立渐近最优性。
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.