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Technometrics Volume 51, 2009 - Issue 4
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Articles

Simultaneous Determination of Tuning and Calibration Parameters for Computer Experiments

Gang Han H. Lee Moffitt Cancer Center & Research Institute, MRC/BIOSTAT, 12902 Magnolia Drive, Tampa, FL 33612
,
Thomas J. Santner Department of Statistics, The Ohio State University, 415 Cockins Hall, 1958 Neil Avenue, Columbus, OH 43210
&
Jeremy J. Rawlinson C2-527 Clinical Programs Center, Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
Pages 464-474 | Received 01 Jul 2008, Published online: 01 Jan 2012

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Rui Tuo, Shiyuan He, Arash Pourhabib, Yu Ding & Jianhua Z. Huang. (2023) A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models. Journal of the American Statistical Association 118:542, pages 883-897.
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Gang Han, Thomas J. Santner, Haiqun Lin & Ao Yuan. (2023) Bayesian-Frequentist Hybrid Inference in Applications with Small Sample Sizes. The American Statistician 77:2, pages 143-150.
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Yun Am Seo, Youngsaeng Lee & Jeong-Soo Park. (2022) Iterative method for tuning complex simulation code. Communications in Statistics - Simulation and Computation 51:7, pages 3975-3992.
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Babak Farmanesh, Arash Pourhabib, Balabhaskar Balasundaram & Austin Buchanan. (2021) A Bayesian framework for functional calibration of expensive computational models through non-isometric matching. IISE Transactions 53:3, pages 352-364.
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Chih-Li Sung, Ying Hung, William Rittase, Cheng Zhu & C. F. J. Wu. (2020) Calibration for Computer Experiments With Binary Responses and Application to Cell Adhesion Study. Journal of the American Statistical Association 115:532, pages 1664-1674.
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Hengzhen Huang, Dennis K. J. Lin, Min-Qian Liu & Qiaozhen Zhang. (2020) Variable selection for kriging in computer experiments. Journal of Quality Technology 52:1, pages 40-53.
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Matthew Plumlee. (2017) Bayesian Calibration of Inexact Computer Models. Journal of the American Statistical Association 112:519, pages 1274-1285.
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Paul D. Arendt, Daniel W. Apley & Wei Chen. (2016) A preposterior analysis to predict identifiability in the experimental calibration of computer models. IIE Transactions 48:1, pages 75-88.
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Arash Pourhabib, Jianhua Z. Huang, Kan Wang, Chuck Zhang, Ben Wang & Yu Ding. (2015) Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables. IIE Transactions 47:2, pages 141-152.
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Arun Kumar. (2015) Sequential tuning of complex computer models. Journal of Statistical Computation and Simulation 85:2, pages 393-404.
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Chia-Jung Chang & V. Roshan Joseph. (2014) Model Calibration Through Minimal Adjustments. Technometrics 56:4, pages 474-482.
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Rui Tuo, C. F. Jeff Wu & Dan Yu. (2014) Surrogate Modeling of Computer Experiments With Different Mesh Densities. Technometrics 56:3, pages 372-380.
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Jeong-Soo Park, Sanghoo Yoon & Dennis D. Cox. (2013) Inverse solution for parameter estimation of computer simulation by an empirical Bayesian code tuning method. Inverse Problems in Science and Engineering 21:3, pages 524-536.
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Shifeng Xiong, PeterZ. G. Qian & C. F. Jeff Wu. (2013) Sequential Design and Analysis of High-Accuracy and Low-Accuracy Computer Codes. Technometrics 55:1, pages 37-46.
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Anton van Beek, Andrea Giuntoli, Nitin K. Hansoge, Sinan Keten & Wei Chen. (2023) Semi-Parametric Functional Calibration Using Uncertainty Quantification Based Decision Support. Journal of Verification, Validation and Uncertainty Quantification 8:2.
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Chih-Li Sung, Beau David BarberBerkley J. Walker. (2022) Calibration of Inexact Computer Models with Heteroscedastic Errors. SIAM/ASA Journal on Uncertainty Quantification 10:4, pages 1733-1752.
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Benedict Götz, Sebastian Kersting & Michael Kohler. (2020) Estimation of an improved surrogate model in uncertainty quantification by neural networks. Annals of the Institute of Statistical Mathematics 73:2, pages 249-281.
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Michael Kohler & Adam Krzyżak. (2021) Estimation of a density using an improved surrogate model. Electronic Journal of Statistics 15:1.
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Yun Am Seo & Jeong-Soo Park. (2020) Expectation-Maximization Algorithm for the Calibration of Complex Simulator Using a Gaussian Process Emulator. Entropy 23:1, pages 53.
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Youngsaeng Lee & Jeong-Soo Park. (2020) Generalized Nonlinear Least Squares Method for the Calibration of Complex Computer Code Using a Gaussian Process Surrogate. Entropy 22:9, pages 985.
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Kathryn A. Maupin & Laura P. Swiler. (2020) Model discrepancy calibration across experimental settings. Reliability Engineering & System Safety 200, pages 106818.
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Xiaoning Kang & Xinwei Deng. (2020) Design and analysis of computer experiments with quantitative and qualitative inputs: A selective review. WIREs Data Mining and Knowledge Discovery 10:3.
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Matthew Plumlee. (2019) Computer model calibration with confidence and consistency. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81:3, pages 519-545.
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Dongdong You, Xiaocheng Shen, Yanghui Zhu, Jianxin Deng & Fenglei Li. (2019) A Quantitative Validation Method of Kriging Metamodel for Injection Mechanism Based on Bayesian Statistical Inference. Metals 9:5, pages 493.
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Michael Kohler & Adam Krzyzak. (2019) Estimation of a Density From an Imperfect Simulation Model. IEEE Transactions on Information Theory 65:3, pages 1535-1546.
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Michael Kohler, Adam Krzyżak, Shashidhar Mallapur & Roland Platz. (2018) Uncertainty Quantification in Case of Imperfect Models: A Non‐Bayesian Approach. Scandinavian Journal of Statistics 45:3, pages 729-752.
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Xu Wu, Tomasz Kozlowski, Hadi Meidani & Koroush Shirvan. (2018) Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory. Nuclear Engineering and Design 335, pages 339-355.
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Guillaume Damblin, Pierre Barbillon, Merlin Keller, Alberto Pasanisi & Éric Parent. (2018) Adaptive Numerical Designs for the Calibration of Computer Codes. SIAM/ASA Journal on Uncertainty Quantification 6:1, pages 151-179.
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Na Qiu, Chanyoung Park, Yunkai Gao, Jianguang Fang, Guangyong Sun & Nam H. Kim. (2018) Sensitivity-Based Parameter Calibration and Model Validation Under Model Error. Journal of Mechanical Design 140:1.
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Thomas J. Santner, Brian J. Williams & William I. NotzThomas J. Santner, Brian J. Williams & William I. Notz. 2018. The Design and Analysis of Computer Experiments. The Design and Analysis of Computer Experiments 299 379 .
Jie Xu. 2017. Advances in Modeling and Simulation. Advances in Modeling and Simulation 27 46 .
Zhen Jiang, Paul D. Arendt, Daniel W. Apley & Wei Chen. 2017. Handbook of Uncertainty Quantification. Handbook of Uncertainty Quantification 69 127 .
Guillaume Damblin, Merlin Keller, Pierre Barbillon, Alberto Pasanisi & Éric Parent. (2016) Bayesian Model Selection for the Validation of Computer Codes. Quality and Reliability Engineering International 32:6, pages 2043-2054.
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Rui Tuo & C. F. Jeff Wu. (2016) A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties. SIAM/ASA Journal on Uncertainty Quantification 4:1, pages 767-795.
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Rui Tuo & C. F. Jeff Wu. (2015) Efficient calibration for imperfect computer models. The Annals of Statistics 43:6.
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Jun Yuan & Szu Hui Ng. (2015) Calibration, Validation, and Prediction in Random Simulation Models. ACM Transactions on Modeling and Computer Simulation 25:3, pages 1-25.
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Zhen Jiang, Paul D. Arendt, Daniel W. Apley & Wei Chen. 2016. Handbook of Uncertainty Quantification. Handbook of Uncertainty Quantification 1 59 .
Serge Guillas, Nina Glover & Liora Malki-Epshtein. (2014) Bayesian calibration of the constants of the – turbulence model for a CFD model of street canyon flow . Computer Methods in Applied Mechanics and Engineering 279, pages 536-553.
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Jun Yuan, Szu Hui Ng & Kwok Leung Tsui. (2013) Calibration of Stochastic Computer Models Using Stochastic Approximation Methods. IEEE Transactions on Automation Science and Engineering 10:1, pages 171-186.
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Wenny Chandra & Dennis K. J. Lin. (2011) Parameter calibration and inadequacy modeling in a computer experiment. Quality and Reliability Engineering International 28:1, pages 35-46.
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Sigal Levy & David M. Steinberg. (2011) Computer experiments: a review. AStA Advances in Statistical Analysis 94:4, pages 311-324.
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