Prediction Based on the Kennedy-O'Hagan Calibration Model: Asymptotic Consistency and Other Properties

• Activity Number: 98
• Type: Invited
• Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Rui Tuo* and Jeff Wu
• Companies: Chinese Academy of Sciences and Georgia Institute of Technology
• Keywords: Computer experiments ; Calibration ; Discrepancy

Abstract

Kennedy and O'Hagan (2001) proposes a model for calibrating some unknown parameters in a computer model and estimating the discrepancy between the computer output and physical response. This model is known to have certain identifiability issue. Tuo and Wu (2015) shows that there are examples for which the KO method renders unreasonable results in calibration. In spite of its unstable performance in calibration, the KO approach has a more robust behavior in predicting the physical response. In this work, we present some theoretical analysis to show the consistency of predictor based on the KO calibration model in the context of radial basis functions. The results are demonstrated with numerical examples.