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Triple Designs: A Closer Look from Indicator Function

Author

Listed:
  • Zujun Ou

    (College of Mathematics and Statistics, Jishou University, Jishou 416000, China)

  • Minghui Zhang

    (College of Mathematics and Statistics, Jishou University, Jishou 416000, China)

  • Hongyi Li

    (College of Mathematics and Statistics, Jishou University, Jishou 416000, China)

Abstract

A method of tripling for a three-level design, which triples both the run size and the number of factors of the initial design, has been proposed for constructing a design that can accommodate a large number of factors by combining all possible level permutations of its initial design. Based on the link between the indicator functions of a triple design and its initial design, the close relationships between a triple design and its initial design are built from properties such as resolution and orthogonality. These theoretical results present a closer look at a triple design and provide a solid foundation for a design constructed using the tripling method, where the constructed designs have better properties, such as high resolution and orthogonality, and are recommended for application in high dimension topics of statistics or large-scale experiments.

Suggested Citation

  • Zujun Ou & Minghui Zhang & Hongyi Li, 2023. "Triple Designs: A Closer Look from Indicator Function," Mathematics, MDPI, vol. 11(3), pages 1-12, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:750-:d:1054957
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    References listed on IDEAS

    as
    1. Yu Tang & Hongquan Xu, 2014. "Permuting regular fractional factorial designs for screening quantitative factors," Biometrika, Biometrika Trust, vol. 101(2), pages 333-350.
    2. N. Balakrishnan & Po Yang, 2006. "Connections Between the Resolutions of General Two-level Factorial Designs," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 609-618, September.
    3. Ou, Zujun & Qin, Hong, 2010. "Some applications of indicator function in two-level factorial designs," Statistics & Probability Letters, Elsevier, vol. 80(1), pages 19-25, January.
    4. N. Balakrishnan & Po Yang, 2006. "Classification of Three-word Indicator Functions of Two-level Factorial Designs," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 595-608, September.
    5. Zujun Ou & Hong Qin, 2017. "Analytic connections between a double design and its original design in terms of different optimality criteria," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(15), pages 7630-7641, August.
    6. Hu, Jianwei & Zhang, Runchu, 2009. "Maximal rank minimum aberration and doubling," Statistics & Probability Letters, Elsevier, vol. 79(7), pages 915-919, April.
    7. 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.
    8. Yuanzhen He & Boxin Tang, 2013. "Strong orthogonal arrays and associated Latin hypercubes for computer experiments," Biometrika, Biometrika Trust, vol. 100(1), pages 254-260.
    9. Li, Hongyi & Qin, Hong, 2018. "Some new results on Triple designs," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 1-9.
    10. Yong-Dao Zhou & Hongquan Xu, 2014. "Space-Filling Fractional Factorial Designs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1134-1144, September.
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    Cited by:

    1. Krahé, Max, 2023. "Italiens Stagnation verstehen," Papers 277907, Dezernat Zukunft - Institute for Macrofinance, Berlin.

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