Non-Uniqueness of E(s2)-Optimal Supersaturated Designs for N ≡ 2 (mod 4) Runs with Application to the Case N = 10 Runs

In factor screening experiments with limited resources, it is common for practitioners to cut down the number of runs N and choose a supersaturated design for the experiment. In the past two decades, E(s2)-optimality has been one of the most important criteria used to choose a supersaturated design. We show that the definition E(s2)-optimal supersaturated designs X for N ≡ 2 (mod 4) runs and m ≥ N factors are not unique by showing that XX⊤ allows for multiple non-isomorphic possibilities for most values of m. For N = 10 and 12 ≤ m ≤ 114 we list all possible E(s2)-optimal designs.

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Publication Details

Journal
Italian Journal of Applied Statistics
Published
2025-09-25
DOI
https://doi.org/10.36253/ijas-16754
Primary Topic
Optimal Experimental Design Methods
Type
article
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article

Non-Uniqueness of E(s2)-Optimal Supersaturated Designs for N ≡ 2 (mod 4) Runs with Application to the Case N = 10 Runs

Michael Neubauer, Francois K. Domagni
Italian Journal of Applied Statistics
Optimal Experimental Design Methods
article

Non-Uniqueness of E(s2)-Optimal Supersaturated Designs for N ≡ 2 (mod 4) Runs with Application to the Case N = 10 Runs

Michael Neubauer, Francois K. Domagni
article en

Abstract

In factor screening experiments with limited resources, it is common for practitioners to cut down the number of runs N and choose a supersaturated design for the experiment. In the past two decades, E(s2)-optimality has been one of the most important criteria used to choose a supersaturated design. We show that the definition E(s2)-optimal supersaturated designs X for N ≡ 2 (mod 4) runs and m ≥ N factors are not unique by showing that XX⊤ allows for multiple non-isomorphic possibilities for most values of m. For N = 10 and 12 ≤ m ≤ 114 we list all possible E(s2)-optimal designs.

Italian Journal of Applied Statistics
California State University, Northridge (US)
Openalex Percentile: Top 67%
Optimal Experimental Design Methods
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