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Maximum projection designs for computer experiments

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  • V. Roshan Joseph
  • Evren Gul
  • Shan Ba

Abstract

Space-filling properties are important in designing computer experiments. The traditional maximin and minimax distance designs consider only space-filling in the full-dimensional space; this can result in poor projections onto lower-dimensional spaces, which is undesirable when only a few factors are active. Restricting maximin distance design to the class of Latin hypercubes can improve one-dimensional projections but cannot guarantee good space-filling properties in larger subspaces. We propose designs that maximize space-filling properties on projections to all subsets of factors. We call our designs maximum projection designs. Our design criterion can be computed at no more cost than a design criterion that ignores projection properties.

Suggested Citation

  • V. Roshan Joseph & Evren Gul & Shan Ba, 2015. "Maximum projection designs for computer experiments," Biometrika, Biometrika Trust, vol. 102(2), pages 371-380.
  • Handle: RePEc:oup:biomet:v:102:y:2015:i:2:p:371-380.
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    File URL: http://hdl.handle.net/10.1093/biomet/asv002
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    Cited by:

    1. Xueru Zhang & Dennis K. J. Lin & Lin Wang, 2023. "Digital Triplet: A Sequential Methodology for Digital Twin Learning," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
    2. Song-Nan Liu & Min-Qian Liu & Jin-Yu Yang, 2023. "Construction of Column-Orthogonal Designs with Two-Dimensional Stratifications," Mathematics, MDPI, vol. 11(6), pages 1-27, March.
    3. Mu, Weiyan & Xiong, Shifeng, 2018. "A class of space-filling designs and their projection properties," Statistics & Probability Letters, Elsevier, vol. 141(C), pages 129-134.
    4. Guillaume Perrin & Christian Soize, 2020. "Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework," Computational Statistics, Springer, vol. 35(1), pages 111-133, March.
    5. Leurent, Martin & Jasserand, Frédéric & Locatelli, Giorgio & Palm, Jenny & Rämä, Miika & Trianni, Andrea, 2017. "Driving forces and obstacles to nuclear cogeneration in Europe: Lessons learnt from Finland," Energy Policy, Elsevier, vol. 107(C), pages 138-150.
    6. Arvind Krishna & Huan Tran & Chaofan Huang & Rampi Ramprasad & V. Roshan Joseph, 2024. "Adaptive Exploration and Optimization of Materials Crystal Structures," INFORMS Joural on Data Science, INFORMS, vol. 3(1), pages 68-83, April.
    7. Hao Chen & Yan Zhang & Xue Yang, 2021. "Uniform projection nested Latin hypercube designs," Statistical Papers, Springer, vol. 62(4), pages 2031-2045, August.
    8. Yang You & Guang Jin & Zhengqiang Pan & Rui Guo, 2021. "MP-CE Method for Space-Filling Design in Constrained Space with Multiple Types of Factors," Mathematics, MDPI, vol. 9(24), pages 1-13, December.
    9. Ray, Douglas & Ramirez-Marquez, Jose, 2020. "A framework for probabilistic model-based engineering and data synthesis," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    10. Guo, Hongqiang & Lu, Silong & Hui, Hongzhong & Bao, Chunjiang & Shangguan, Jinyong, 2019. "Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass," Energy, Elsevier, vol. 176(C), pages 292-308.
    11. Francisco Castillo-Zunino & Pinar Keskinocak, 2021. "Bi-criteria multiple knapsack problem with grouped items," Journal of Heuristics, Springer, vol. 27(5), pages 747-789, October.
    12. Yue Huan & Yubin Tian & Dianpeng Wang, 2022. "A Weighted Surrogate Model for Spatio-Temporal Dynamics with Multiple Time Spans: Applications for the Pollutant Concentration of the Bai River," Mathematics, MDPI, vol. 10(19), pages 1-16, October.
    13. Roy, Pamphile T. & Jofre, Lluís & Jouhaud, Jean-Christophe & Cuenot, Bénédicte, 2020. "Versatile sequential sampling algorithm using Kernel Density Estimation," European Journal of Operational Research, Elsevier, vol. 284(1), pages 201-211.
    14. Tonghui Pang & Yan Wang & Jian-Feng Yang, 2022. "Asymptotically optimal maximin distance Latin hypercube designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(4), pages 405-418, May.

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