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Prediction for Big Data through Kriging : Small Sequential and One-Shot Designs

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  • Kleijnen, J.P.C.

    (Tilburg University, Center For Economic Research)

  • van Beers, W.C.M.

    (Tilburg University, Center For Economic Research)

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  • Kleijnen, J.P.C. & van Beers, W.C.M., 2018. "Prediction for Big Data through Kriging : Small Sequential and One-Shot Designs," Discussion Paper 2018-022, Tilburg University, Center for Economic Research.
    • Handle: RePEc:tiu:tiucen:b0504930-f518-44f7-908c-6a147cef26bd
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    References listed on IDEAS

    as
    1. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    2. Kleijnen, J.P.C., 2017. "Design and Analysis of simulation experiments : Tutorial," Other publications TiSEM c7ad6b68-dcd6-4485-9ee2-0, Tilburg University, School of Economics and Management.
    3. Gramacy, Robert B., 2016. "laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i01).
    4. 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, September.
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    Cited by:

    1. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.

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    Keywords

    kriging; Gaussian process; big data; experimental design; nearest neighbor;
    All these keywords.

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