ADAPTIVE DESIGN FOR GAUSSIAN PROCESS REGRESSION UNDER CENSORING

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 max-imizing predictive power using a small number of expensive experimental runs. To tackle this, we propose a novel adaptive design method, called the in-tegrated 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 effective-ness of the ICMSE design in two real-world applications on surgical planning and wafer manufacturing.

Duke Scholars

Author Simon Mak Statistical Science