SATURATED LOCALLY OPTIMAL DESIGNS UNDER DIFFERENTIABLE OPTIMALITY CRITERIA

• 成果类型: Article
• 署名作者: Hu, Linwei; Yang, Min; Stufken, John
• 署名单位: University System of Georgia; University of Georgia; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Arizona State University; Arizona State University-Tempe
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/14-AOS1263
• 发表日期: 2015
• 页码: 30-56
• 关键词: la garza phenomenon; nonlinear models; regression-models; points

摘要

We develop general theory for finding locally optimal designs in a class of single-covariate models under any differentiable optimality criterion. Yang and Stuficen [Ann. Statist. 40 (2012) 1665-1681] and Dette and Schorning [Ann. Statist. 41 (2013) 1260-1267] gave complete class results for optimal designs under such models. Based on their results, saturated optimal designs exist; however, how to find such designs has not been addressed. We develop tools to find saturated optimal designs, and also prove their uniqueness under mild conditions.