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Sequential Supersaturated Screening Experiments — arXiv:2609.37756

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37756
License (as archived): CC0-1.0
Archived: 2026-10-07T18:00:56.822Z
SHA-256 of the archived text: 8d55d5e8e3df195bd232d7d4fedf2c97e2f2a0b4ed3f7ca137ac498b6c6ad60c
Stable URL of this page: https://metraipolis.com/documento/dc_4c3236a040ae

How to cite

Sequential Supersaturated Screening Experiments — arXiv:2609.37756 — arXiv (en), https://arxiv.org/abs/2609.37756 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4c3236a040ae (Metraipolis archive dc_4c3236a040ae, sha256 8d55d5e8e3df195bd232d7d4fedf2c97e2f2a0b4ed3f7ca137ac498b6c6ad60c)

BibTeX

@misc{dc4c3236a040ae,
  title = {Sequential Supersaturated Screening Experiments — arXiv:2609.37756},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37756},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4c3236a040ae; sha256 8d55d5e8e3df195bd232d7d4fedf2c97e2f2a0b4ed3f7ca137ac498b6c6ad60c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37756. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Supersaturated screening experiments study many candidate factors with few runs. The experimenter must first identify the active factors, then optimize the response. One-shot regularized regression tends to select many factors, and the second-order response surface model in the selected factors is then large and needs many further runs to fit. We propose Sequential Supersaturated Screening ($S^3$), a two-stage framework for supersaturated designs. $S^3$ builds each screening round by coordinate exchange under a new positive-cone design criterion that uses no practitioner-chosen Welch calibration constant. It then removes low-importance factors one round at a time, using a graduated quantile rule. The criterion scores a design by how far its column correlations sit above the Welch lower bound, and it adapts to the current number of runs and candidate factors. We give a round-by-round bound on the probability that a noise factor is ever fixed, under conditions on noise survival and commitment at each round, together with an explicit upper bound on the total number of Stage 1 runs. Across simulated screening problems and the Borehole benchmark, $S^3$ achieves lower Type I error and higher $F_1$ scores than one-shot cross-validated Lasso (LassoCV). It also improves optimization quality in most settings and runs faster.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in stat.ME).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.37756 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T17:33:58.774Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).