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Blocked factor aliased effect-number pattern and column rank of blocked regular designs

Author

Listed:
  • Dongying Wang

    (Northeast Normal University)

  • Shili Ye

    (Northeast Normal University)

  • Qi Zhou

    (Tianjin University of Finance and Economics)

  • Runchu Zhang

    (Northeast Normal University
    Nankai University
    University of British Columbia)

Abstract

In factorial experiments, estimation precision of specific factor effects depends not only on design selection but also on factor assignments to columns of selected designs. Usually, different columns in a design play different roles when estimating factor effects. Zhou et al. (Can J Stat 41:540-555, 2013) introduced a factor aliased effect-number pattern (F-AENP) and proposed a column ranking scheme for all the GMC $$2^{n-m}$$ 2 n - m designs with $$5N/16+1\le n\le N-1$$ 5 N / 16 + 1 ≤ n ≤ N - 1 , where $$N=2^{n-m}$$ N = 2 n - m . In this paper, we first introduce a blocked factor aliased effect-number pattern (B-F-AENP) for blocked regular designs as an extension of the F-AENP. Then, by using the B-F-AENP, we propose a column ranking scheme for all the B $$^1$$ 1 -GMC $$2^{n-m}:2^s$$ 2 n - m : 2 s designs with $$5N/16+1\le n\le N-1$$ 5 N / 16 + 1 ≤ n ≤ N - 1 , as well as an assignment strategy for important factors.

Suggested Citation

  • Dongying Wang & Shili Ye & Qi Zhou & Runchu Zhang, 2017. "Blocked factor aliased effect-number pattern and column rank of blocked regular designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(2), pages 133-152, February.
    • Handle: RePEc:spr:metrik:v:80:y:2017:i:2:d:10.1007_s00184-016-0595-7
    DOI: 10.1007/s00184-016-0595-7
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