Open Access
December 2015 Efficient calibration for imperfect computer models
Rui Tuo, C. F. Jeff Wu
Ann. Statist. 43(6): 2331-2352 (December 2015). DOI: 10.1214/15-AOS1314


Many computer models contain unknown parameters which need to be estimated using physical observations. Tuo and Wu (2014) show that the calibration method based on Gaussian process models proposed by Kennedy and O’Hagan [J. R. Stat. Soc. Ser. B. Stat. Methodol. 63 (2001) 425–464] may lead to an unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the $L_{2}$ calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Numerical examples show that the proposed method outperforms the existing ones.


Download Citation

Rui Tuo. C. F. Jeff Wu. "Efficient calibration for imperfect computer models." Ann. Statist. 43 (6) 2331 - 2352, December 2015.


Received: 1 April 2014; Revised: 1 January 2015; Published: December 2015
First available in Project Euclid: 7 October 2015

zbMATH: 1326.62228
MathSciNet: MR3405596
Digital Object Identifier: 10.1214/15-AOS1314

Primary: 62A01 , 62P30
Secondary: 62F12

Keywords: computer experiments , ‎reproducing kernel Hilbert ‎space , Semiparametric efficiency , uncertainty quantification

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.43 • No. 6 • December 2015
Back to Top