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Bayesian-inspired minimum aberration two- and four-level designs

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  • V. Roshan Joseph
  • Mingyao AI
  • C. F. Jeff Wu

Abstract

Motivated by a Bayesian framework, we propose a new minimum aberration-type criterion for designing experiments with two- and four-level factors. The Bayesian approach helps in overcoming the ad hoc nature of effect ordering in the existing minimum aberration-type criteria. The approach is also capable of distinguishing between qualitative and quantitative factors. Numerous examples are given to demonstrate its advantages. Copyright 2009, Oxford University Press.

Suggested Citation

  • V. Roshan Joseph & Mingyao AI & C. F. Jeff Wu, 2009. "Bayesian-inspired minimum aberration two- and four-level designs," Biometrika, Biometrika Trust, vol. 96(1), pages 95-106.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:1:p:95-106
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    File URL: http://hdl.handle.net/10.1093/biomet/asn062
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    Cited by:

    1. Hui Li & Min-Qian Liu & Jinyu Yang, 2024. "Bayesian minimum aberration mixed-level split-plot designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(7), pages 889-906, October.

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