ROBUST DISCRIMINATION DESIGNS OVER HELLINGER NEIGHBOURHOODS

• 成果类型: Article
• 署名作者: Hu, Rui; Wiens, Douglas P.
• 署名单位: MacEwan University; University of Alberta
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/16-AOS1503
• 发表日期: 2017
• 页码: 1638-1663
• 关键词: 2 rival models; regression

摘要

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.