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Semifoldover plans for three-level orthogonal arrays with quantitative factors

Author

Listed:
  • Wenlong Li

    (Nankai University)

  • Bing Guo

    (Sichuan University)

  • Hengzhen Huang

    (Guangxi Normal University)

  • Min-Qian Liu

    (Nankai University)

Abstract

Although foldover designs can de-alias many effects, they involve at least twice the original number of runs. A semifoldover design, one kind of the partial foldover designs, is typically much more efficient since such a design adds only half of the new runs of a foldover design to the initial design. Semifoldover designs for two-level orthogonal arrays have been investigated in recent literatures. With the use of linear-quadratic system, this paper considers semifoldover designs for three-level orthogonal arrays with quantitative factors. We examine when the linear effects can be de-aliased from their aliased two-factor interactions for regular and nonregular designs, and obtain some good properties via semifolding over on partial factors or all factors. Theoretical properties and some examples are provided to illustrate the usefulness of the proposed designs.

Suggested Citation

  • Wenlong Li & Bing Guo & Hengzhen Huang & Min-Qian Liu, 2021. "Semifoldover plans for three-level orthogonal arrays with quantitative factors," Statistical Papers, Springer, vol. 62(6), pages 2691-2709, December.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:6:d:10.1007_s00362-020-01211-2
    DOI: 10.1007/s00362-020-01211-2
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    References listed on IDEAS

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    1. Arman Sabbaghi & Tirthankar Dasgupta & C. F. Jeff Wu, 2014. "Indicator functions and the algebra of the linear-quadratic parameterization," Biometrika, Biometrika Trust, vol. 101(2), pages 351-363.
    2. Ai, Mingyao & Hickernell, Fred J. & Lin, Dennis K.J., 2008. "Optimal foldover plans for regular s-level fractional factorial designs," Statistics & Probability Letters, Elsevier, vol. 78(7), pages 896-903, May.
    3. Po Yang & Chang-Yun Lin & William Li, 2015. "Blocked semifoldovers of two-level orthogonal designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(5), pages 529-548, July.
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