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Computer algorithms of lower-order confounding in regular designs

Author

Listed:
  • Zhi Li

    (Xinjiang University)

  • Zhiming Li

    (Xinjiang University)

Abstract

In the design of experiments, an optimal design should minimize the confounding between factorial effects, especially main effects and two-factor interaction effects. The general minimum lower-order confounding (GMC) criterion can be used to choose optimal regular designs based on the aliased component-number pattern. This paper aims to study the confounding properties of lower-order effects and provide several computer algorithms to calculate the lower-order confounding in regular designs. We provide a search algorithm to obtain GMC designs. Through python software, we conduct these algorithms. Some examples are analyzed to illustrate the effectiveness of the proposed algorithms.

Suggested Citation

  • Zhi Li & Zhiming Li, 2024. "Computer algorithms of lower-order confounding in regular designs," Computational Statistics, Springer, vol. 39(2), pages 653-676, April.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:2:d:10.1007_s00180-022-01315-3
    DOI: 10.1007/s00180-022-01315-3
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    References listed on IDEAS

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    1. Li, Zhiming & Zhao, Shengli & Zhang, Runchu, 2015. "On general minimum lower order confounding criterion for s-level regular designs," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 202-209.
    2. Jia-Lin Wei & Jian-Feng Yang, 2013. "On simplifying the calculations leading to designs with general minimum lower-order confounding," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(5), pages 723-732, July.
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