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Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039)

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  • Kleijnen, Jack P.C.

    (Tilburg University, School of Economics and Management)

  • Mehdad, E.

    (Tilburg University, School of Economics and Management)

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  • Kleijnen, Jack P.C. & Mehdad, E., 2014. "Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039)," Other publications TiSEM 8a096696-f700-4cbe-9474-c, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:8a096696-f700-4cbe-9474-c6e93f5e323b
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    References listed on IDEAS

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    1. D den Hertog & J P C Kleijnen & A Y D Siem, 2006. "The correct Kriging variance estimated by bootstrapping," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 400-409, April.
    2. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Other publications TiSEM e49ba0fc-853c-4a13-b564-d, Tilburg University, School of Economics and Management.
    3. J P C Kleijnen & M T Smits, 2003. "Performance metrics in supply chain management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(5), pages 507-514, May.
    4. Kleijnen, Jack P.C. & Mehdad, E., 2013. "Conditional simulation for efficient global optimization," Other publications TiSEM 52e4860d-9887-4a63-b19a-7, Tilburg University, School of Economics and Management.
    5. Kleijnen, J.P.C. & Smits, M.T., 2003. "Performance metrics in supply chain management," Other publications TiSEM 80777aed-0c9f-4ded-b0bb-f, Tilburg University, School of Economics and Management.
    6. Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
    7. Jack P.C. Kleijnen, 2015. "Design and Analysis of Simulation Experiments," International Series in Operations Research and Management Science, Springer, edition 2, number 978-3-319-18087-8, April.
    8. van Ham, G. & Rotmans, J. & Kleijnen, J.P.C., 1992. "Techniques for sensitivity analysis of simulation models : A case study of the CO2 greenhouse effect," Other publications TiSEM 71317a03-3399-4554-83cb-4, Tilburg University, School of Economics and Management.
    9. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    10. Hernandez, Andres F. & Grover, Martha A., 2013. "Error estimation properties of Gaussian process models in stochastic simulations," European Journal of Operational Research, Elsevier, vol. 228(1), pages 131-140.
    11. Kleijnen, Jack P.C. & Beers, Wim van & Nieuwenhuyse, Inneke van, 2010. "Constrained optimization in expensive simulation: Novel approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 164-174, April.
    12. Gneiting, Tilmann & Kleiber, William & Schlather, Martin, 2010. "Matérn Cross-Covariance Functions for Multivariate Random Fields," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1167-1177.
    13. Xi Chen & Bruce E. Ankenman & Barry L. Nelson, 2013. "Enhancing Stochastic Kriging Metamodels with Gradient Estimators," Operations Research, INFORMS, vol. 61(2), pages 512-528, April.
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    Cited by:

    1. Sadoughi, Mohammadkazem & Li, Meng & Hu, Chao, 2018. "Multivariate system reliability analysis considering highly nonlinear and dependent safety events," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 189-200.
    2. Furtado Júnior, Juarez Corrêa & Palacio, José Carlos Escobar & Leme, Rafael Coradi & Lora, Electo Eduardo Silva & da Costa, José Eduardo Loureiro & Reyes, Arnaldo Martín Martínez & del Olmo, Oscar Alm, 2020. "Biorefineries productive alternatives optimization in the brazilian sugar and alcohol industry," Applied Energy, Elsevier, vol. 259(C).
    3. Binois, M. & Ginsbourger, D. & Roustant, O., 2015. "Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations," European Journal of Operational Research, Elsevier, vol. 243(2), pages 386-394.
    4. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    5. Liu, Yushan & Li, Luyi & Zhao, Sihan & Song, Shufang, 2021. "A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    6. Rahat, Alma A.M. & Wang, Chunlin & Everson, Richard M. & Fieldsend, Jonathan E., 2018. "Data-driven multi-objective optimisation of coal-fired boiler combustion systems," Applied Energy, Elsevier, vol. 229(C), pages 446-458.

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