Robustness of classical and optimal designs to missing observations

Byran J. Smucker, Willis Jensen, Zichen Wu and Bo Wang

Computational Statistics & Data Analysis, 2017, vol. 113, issue C, 251-260

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.

Keywords: Central composite designs; Factorial designs; Missing data; D-efficiency; I-efficiency (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:113:y:2017:i:c:p:251-260

DOI: 10.1016/j.csda.2016.12.001

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