On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm

• 成果类型: Article
• 署名作者: Yang, Min; Biedermann, Stefanie; Tang, Elina
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Southampton; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.806268
• 发表日期: 2013
• 页码: 1411-1420
• 关键词: locally optimal designs; la garza phenomenon; support-points

摘要

Finding optimal designs for nonlinear models is challenging in general. Although some recent results allow us to focus on a simple subclass of designs for most problems, deriving a specific optimal design still mainly depends on numerical approaches. There is need for a general and efficient algorithm that is more broadly applicable than the current state-of-the-art methods. We present a new algorithm that can be used to find optimal designs with respect to a broad class of optimality criteria, when the model parameters or functions thereof are of interest, and for both locally optimal and multistage design strategies. We prove convergence to the optimal design, and show in various examples that the new algorithm outperforms the current state-of-the-art algorithms.