ASYMPTOTIC BAYES CRITERIA FOR NONPARAMETRIC RESPONSE-SURFACE DESIGN

• 成果类型: Article
• 署名作者: MITCHELL, T; SACKS, J; YLVISAKER, D
• 署名单位: United States Department of Energy (DOE); Oak Ridge National Laboratory; University of California System; University of California Los Angeles
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176325488
• 发表日期: 1994
• 页码: 634-651
• 关键词: LINEAR-REGRESSION; models

摘要

This paper deals with Bayesian design for response surface prediction when the prior may be finite or infinite dimensional, the design space arbitrary. In order that the resulting problems be manageable, we resort to asymptotic versions of D-, G- and A-optimality. Here the asymptotics stem from allowing the error variance to be large. The problems thus elicited have strong game-like characteristics. Examples of theoretical solutions are brought forward, especially when the priors are stationary processes on an interval, and we give numerical evidence that the asymptotics work well in the finite domain.