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Robustness of classical and optimal designs to missing observations

Author

Listed:
  • Smucker, Byran J.
  • Jensen, Willis
  • Wu, Zichen
  • Wang, Bo

Abstract

Missing observations are not uncommon in real-world experiments. Consequently, the robustness of an experimental design to one or more missing runs is an important characteristic of the design. Results of an evaluation of the robustness of classical and optimal designs to missing observations are presented, and optimal designs fare relatively well in terms of robustness compared to classical designs. Additionally, a modified version of an existing robustness criterion is used to construct designs that are robust to missing observations.

Suggested Citation

  • Smucker, Byran J. & Jensen, Willis & Wu, Zichen & Wang, Bo, 2017. "Robustness of classical and optimal designs to missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 251-260.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:251-260
    DOI: 10.1016/j.csda.2016.12.001
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    References listed on IDEAS

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    1. Steven G. Gilmour & Luzia A. Trinca, 2012. "Optimum design of experiments for statistical inference," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(3), pages 345-401, May.
    2. Steven G. Gilmour, 2006. "Response Surface Designs for Experiments in Bioprocessing," Biometrics, The International Biometric Society, vol. 62(2), pages 323-331, June.
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