ADAPTIVE DESIGN FOR GAUSSIAN PROCESS REGRESSION UNDER CENSORING

• 成果类型: Article
• 署名作者: Chen, Jialei; Mak, Simon; Joseph, V. Roshan; Zhang, Chuck
• 署名单位: University System of Georgia; Georgia Institute of Technology; Duke University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1512
• 发表日期: 2022
• 页码: 744-764
• 关键词: sequential design; computer; calibration; prediction

摘要

A key objective in engineering problems is to predict an unknown experimental surface over an input domain. In complex physical experiments this may be hampered by response censoring which results in a significant loss of information. For such problems, experimental design is paramount for maximizing predictive power using a small number of expensive experimental runs. To tackle this, we propose a novel adaptive design method, called the integrated censored mean-squared error (ICMSE) method. The ICMSE method first estimates the posterior probability of a new observation being censored, then adaptively chooses design points that minimize predictive uncertainty under censoring. Adopting a Gaussian process regression model with product correlation function, the proposed ICMSE criterion is easy to evaluate which allows for efficient design optimization. We demonstrate the effectiveness of the ICMSE design in two real-world applications on surgical planning and wafer manufacturing.