PREDICTION BASED ON THE KENNEDY-O'HAGAN CALIBRATION MODEL: ASYMPTOTIC CONSISTENCY AND OTHER PROPERTIES

abstract

• 2018 Institute of Statistical Science. All rights reserved. Kennedy and O'Hagan (2001) propose 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 issues. Tuo and Wu (2016) show that there are examples for which the Kennedy-O'Hagan method renders unreasonable results in calibration. In spite of its unstable performance in calibration, the Kennedy-O'Hagan 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 their calibration model in the context of radial basis functions.

authors

• Tuo, Rui

published proceedings

• STATISTICA SINICA

author list (cited authors)

• Tuo, R., & Wu, C.

citation count

• 7

complete list of authors

• Tuo, Rui||Wu, CF Jeff

publication date

• April 2018

publisher

• Statistica Sinica (Institute of Statistical Science)  Publisher

published in

• STATISTICA SINICA  Journal

keywords

• Bayesian Inference
• Computer Experiments
• Kriging

Digital Object Identifier (DOI)

• 10.5705/ss.202016.0209

start page

• 743

end page

• 759

volume

• 28

issue

• 2

URL

• http://dx.doi.org/10.5705/ss.202016.0209