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Experimentation order with good properties for 2k factorial designs

Author

Listed:
  • Alexander Correa
  • Pere Grima
  • Xavier Tort-Martorell

Abstract

Randomizing the order of experimentation in a factorial design does not always achieve the desired effect of neutralizing the influence of unknown factors. In fact, with some very reasonable assumptions, an important proportion of random orders achieve the same degree of protection as that obtained by experimenting in the design matrix standard order. In addition, randomization can induce a large number of changes in factor levels and thus make experimentation expensive and difficult. De Leon et al. [Experimentation order in factorial designs with 8 or 16 runs, J. Appl. Stat. 32 (2005), pp. 297-313] proposed experimentation orders for designs with eight or 16 runs that combine an excellent level of protection against the influence of unknown factors, with the minimum number of changes in factor levels. This article presents a new methodology to obtain experimentation orders with the desired properties for designs with any number of runs.

Suggested Citation

  • Alexander Correa & Pere Grima & Xavier Tort-Martorell, 2009. "Experimentation order with good properties for 2k factorial designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(7), pages 743-754.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:743-754
    DOI: 10.1080/02664760802499337
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    Cited by:

    1. Hilow, Hisham, 2013. "Comparison among run order algorithms for sequential factorial experiments," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 397-406.
    2. Alexander A. Correa & Pere Grima & Xavier Tort-Martorell, 2012. "Experimentation order in factorial designs: new findings," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1577-1591, January.

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