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Construction of Latin hypercube designs with nested and sliced structures

Author

Listed:
  • Bing Guo

    (Nankai University)

  • Xue-Ping Chen

    (Nankai University
    Jiangsu University of Technology)

  • Min-Qian Liu

    (Nankai University)

Abstract

Recently, the construction of nested or sliced Latin hypercube designs (LHDs) has received notable interest for planning computer experiments with special combinational structures. In this paper, we propose an approach to constructing nested and/or sliced LHDs by using small LHDs and structural vectors/matrices. This method is easy to implement, and can generate nested and sliced LHDs through a unified algorithm. Moreover, an algorithm for improving the space-filling properties of the resulting designs is developed, and under some control the orthogonality of the constructed designs are attainable. Some examples are provided for illustrating the proposed algorithms.

Suggested Citation

  • Bing Guo & Xue-Ping Chen & Min-Qian Liu, 2020. "Construction of Latin hypercube designs with nested and sliced structures," Statistical Papers, Springer, vol. 61(2), pages 727-740, April.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:2:d:10.1007_s00362-017-0959-8
    DOI: 10.1007/s00362-017-0959-8
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    References listed on IDEAS

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    1. Hao Chen & Min-Qian Liu, 2015. "Nested Latin Hypercube Designs with Sliced Structures," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(22), pages 4721-4733, November.
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    Cited by:

    1. Bing Guo & Xiao-Rong Li & Min-Qian Liu & Xue Yang, 2023. "Construction of orthogonal general sliced Latin hypercube designs," Statistical Papers, Springer, vol. 64(3), pages 987-1014, June.
    2. Weiyan Mu & Chengxin Liu & Shifeng Xiong, 2023. "Nested Maximum Entropy Designs for Computer Experiments," Mathematics, MDPI, vol. 11(16), pages 1-12, August.
    3. Wang, Xiaodi & Huang, Hengzhen, 2023. "Group symmetric Latin hypercube designs for symmetrical global sensitivity analysis," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).

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