Model Calibration With Censored Data

ABSTRACT

The purpose of model calibration is to make the model predictions closer to reality. The classical Kennedy–O’Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the Kennedy–O’Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied to study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large. Supplementary materials for this article are available online.

KEYWORDS:

• Bayesian calibration
• Computer experiments
• Gaussian process
• Model discrepancy

Supplementary Materials

This document gives the details of the approximate method using mixture normal distributions.

Additional information

Funding

This research is supported by the U.S. Department of Energy Advanced Scientific Computing Research Award ERKJ259 and a U.S. National Science Foundation grant CMMI-1266025.