{
  "abstract": "Space-filling designs support flexible modeling in noisy multi-response experiments, but an individual design can give poor precision for low-order effects. We use a low-order working model to identify effects whose estimation precision we seek to protect, while allowing a more complex final response model. The filter selects one complete design from a candidate pool by minimizing a geometric loss divided by sphericity, without exchanging or altering its points. Sphericity measures the balance of the working-model confidence ellipsoid's principal-axis variances, independently of its volume. In a working-model basis orthonormal over the prediction region, sphericity is the shape factor of the I-criterion, which measures average prediction variance. In cube studies with quadratic least-squares and Gaussian-process fits, this target filter gives modest average prediction gains over the geometrically best candidate at small geometric cost. Gains in working-model precision are largest near saturation: in a four-factor example, worst-decile prediction standard error falls by about a quarter relative to the geometrically best candidate. In constrained mixtures, raw coefficient-based filtering and A-optimality can emphasize extrapolated Scheffe coefficients and yield poor regional prediction precision. Target filtering improves regional working-model precision in most settings studied, mirroring the advantage of I- over A-optimality for highly constrained mixture designs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36012",
  "author_total": 1,
  "authors": [
    {
      "name": "Andrew T. Karl"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/63a4zexqkt2c9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36012",
  "fulltext_url": null,
  "id": "63a4zexqkt2c9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36012",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1803|Optimal Experimental Design Methods"
    ]
  ],
  "openalex": "W7224121721",
  "pdf_url": "https://arxiv.org/pdf/2610.36012",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/63a4zexqkt2c9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:59Z",
      "native_id": "2610.36012",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36012"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "A Sphericity Filter for Space-Filling Designs in Nondeterministic Experiments",
  "title_text": "A Sphericity Filter for Space-Filling Designs in Nondeterministic Experiments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:59Z",
  "url": "https://research.draconitic.com/paper/63a4zexqkt2c9",
  "venue": "arXiv",
  "versions": []
}