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<p>Gauss-Dantzig Selector-Aggregation over Random Models (GDS-ARM) algorithm is a powerful method for screening main-effects and two-factor interactions in supersaturated designs. Its application, however, has been limited to two-level designs, and its critical tuning parameters are not optimized for the more complex mixed-level context. This paper provides a systematic tuning guide for extending GDS-ARM to mixed-level designs. Through extensive simulations using a diverse set of designs, we sequentially analyze the four key tuning parameters of GDS-ARM. Additionally, we identify a critical characteristic of the design matrix, the ratio of main-effect columns to runs, and provide a practical guideline for design selection based on this metric. The robustness of this complete set of recommendations is confirmed through a validation study on a large design with 140 runs.</p>

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An Extension of the GDS-ARM Algorithm for Factor Screening in Mixed-Level Supersaturated Designs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Fan Zhang,
  • Rakhi Singh,
  • John Stufken

摘要

Gauss-Dantzig Selector-Aggregation over Random Models (GDS-ARM) algorithm is a powerful method for screening main-effects and two-factor interactions in supersaturated designs. Its application, however, has been limited to two-level designs, and its critical tuning parameters are not optimized for the more complex mixed-level context. This paper provides a systematic tuning guide for extending GDS-ARM to mixed-level designs. Through extensive simulations using a diverse set of designs, we sequentially analyze the four key tuning parameters of GDS-ARM. Additionally, we identify a critical characteristic of the design matrix, the ratio of main-effect columns to runs, and provide a practical guideline for design selection based on this metric. The robustness of this complete set of recommendations is confirmed through a validation study on a large design with 140 runs.