Model Calibration with Censored Data

• Activity Number: 279 - Technometrics Invited Paper Session
• Type: Invited
• Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Technometrics
• Author(s): Shan Ba* and Fang Cao and William Brenneman and Roshan Joseph Vengazhiyil
• Companies: The Procter & Gamble Company and Georgia Institute of Technology and The Procter & Gamble Company and Georgia Institute of Technology
• Keywords: Bayesian calibration; Computer experiments; Gaussian process; Model discrepancy

Abstract

The purpose of model calibration is to make the model predictions closer to reality. The classical Kennedy-O'Hagan approach for model calibration accounts for the inadequacy of the computer model while simultaneously estimating any 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.