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Mixture discrepancy on symmetric balanced designs

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  • Elsawah, A.M.
  • Qin, Hong

Abstract

Mixture discrepancy is more reasonable than other discrepancies for measuring the uniformity from different aspects such as the intuitive view, the uniformity of sub-dimension projection, the curse of dimensionality and the geometric property of the kernel function. In this paper, we discuss in depth the mixture discrepancy as the uniformity measure for symmetric two-, three- and four-level balanced designs.

Suggested Citation

  • Elsawah, A.M. & Qin, Hong, 2015. "Mixture discrepancy on symmetric balanced designs," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 123-132.
    • Handle: RePEc:eee:stapro:v:104:y:2015:i:c:p:123-132
    DOI: 10.1016/j.spl.2015.05.007
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    References listed on IDEAS

    as
    1. Elsawah, A.M. & Qin, Hong, 2015. "A new strategy for optimal foldover two-level designs," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 116-126.
    2. Elsawah, A.M. & Qin, Hong, 2015. "Lee discrepancy on symmetric three-level combined designs," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 273-280.
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    Cited by:

    1. Elsawah, A.M., 2016. "Constructing optimal asymmetric combined designs via Lee discrepancy," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 24-31.
    2. A. M. Elsawah & Hong Qin, 2016. "Asymmetric uniform designs based on mixture discrepancy," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2280-2294, September.

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    2. A. M. Elsawah & Hong Qin, 2016. "Asymmetric uniform designs based on mixture discrepancy," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2280-2294, September.
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