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Title: Efficient calibration for imperfect computer models

Abstract

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to 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. Here, numerical examples show that the proposed method outperforms the existing ones.

Authors:
 [1];  [2]
  1. Chinese Academy of Sciences (CAS), Beijing (China)
  2. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Research Org.:
Georgia Tech Research Corp., Atlanta, GA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1405172
Report Number(s):
DOE-GT-0010548-4
Journal ID: ISSN 0090-5364; FG02-13ER26159; TRN: US1702896
Grant/Contract Number:  
SC0010548
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Annals of Statistics
Additional Journal Information:
Journal Volume: 43; Journal Issue: 6; Journal ID: ISSN 0090-5364
Publisher:
Institute of Mathematical Statistics
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Tuo, Rui, and Wu, C. F. Jeff. Efficient calibration for imperfect computer models. United States: N. p., 2015. Web. doi:10.1214/15-AOS1314.
Tuo, Rui, & Wu, C. F. Jeff. Efficient calibration for imperfect computer models. United States. doi:10.1214/15-AOS1314.
Tuo, Rui, and Wu, C. F. Jeff. Tue . "Efficient calibration for imperfect computer models". United States. doi:10.1214/15-AOS1314. https://www.osti.gov/servlets/purl/1405172.
@article{osti_1405172,
title = {Efficient calibration for imperfect computer models},
author = {Tuo, Rui and Wu, C. F. Jeff},
abstractNote = {Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to 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 L2 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. Here, numerical examples show that the proposed method outperforms the existing ones.},
doi = {10.1214/15-AOS1314},
journal = {Annals of Statistics},
issn = {0090-5364},
number = 6,
volume = 43,
place = {United States},
year = {2015},
month = {12}
}

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Cited by: 19 works
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Works referencing / citing this record:

Model Calibration With Censored Data
journal, June 2017


Sequential Design for Functional Calibration of Computer Models
journal, June 2018


Bayesian Calibration of Inexact Computer Models
journal, July 2016


On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
journal, May 2019


A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series
journal, May 2019


Model Calibration With Censored Data
journal, June 2017


Sequential Design for Functional Calibration of Computer Models
journal, June 2018


Bayesian Calibration of Inexact Computer Models
journal, July 2016


On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
journal, May 2019


A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series
journal, May 2019