Journal of Systems Science and Complexity

, Volume 31, Issue 3, pp 773–786 | Cite as

On Construction of Optimal Two-Level Designs with Multi Block Variables

Article

Abstract

When running an experiment, inhomogeneity of the experimental units may result in poor estimations of treatment effects. Thus, it is desirable to select a good blocked design before running the experiment. Mostly, a single block variable was used in the literature to treat the inhomogeneity for simplicity. However, in practice, the inhomogeneity often comes from multi block variables. Recently, a new criterion called B2-GMC was proposed for two-level regular designs with multi block variables. This paper proposes a systematic theory on constructing some B2-GMC designs for the first time. Experimenters can easily obtain the B2-GMC designs according to the construction method. Pros of B2-GMC designs are highlighted in Section 4, and the designs with small run sizes are tabulated in Appendix B for practical use.

Keywords

Blocked design general minimum lower order confounding multi block variables Yates order 

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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.LPMC and Institute of StatisticsNankai UniversityTianjinChina
  2. 2.School of StatisticsQufu Normal UniversityQufuChina

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