{
  "abstract": "Confirmatory trials may seek efficacy claims in a prespecified subgroup and the full popu- lation, although benefit outside the subgroup is uncertain. We measure power loss relative to the marginal level-α test available if the population to protect were known, using the same experiment. In a bivariate Gaussian model with known covariance, we minimise the largest power loss, or regret, over prespecified ranges of target and non-target effects, subject to strong familywise error control and coordinatewise monotonicity. We optimise over monotone closed- testing procedures with fixed marginal tests, allowing randomisation in the shared intersection test. Monotonicity places the worst non-target mean at its lower planning bound for each target effect. On the resulting reduced planning set, every least-favourable distribution is supported on exactly two efficacy scenarios, one for each target population. Under a condition on the effect ranges and covariance, the minimax procedure is nonrandomised and unique up to null sets. An integral criterion characterises exactly when a calibrated step-up procedure attains the minimax value within this class. Bounds for the continuous problem quantify possible improve- ments over simple procedures. An asthma planning example illustrates power allocation and the compatibility of target assignments with treatment benefits and burdens.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03155",
  "author_total": 2,
  "authors": [
    {
      "name": "Lei Li"
    },
    {
      "name": "Yuexiao Dong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1zj4dabnj8a4w.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03155",
  "fulltext_url": null,
  "id": "1zj4dabnj8a4w",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03155",
  "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",
      "math.ST"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.TH"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03155",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1zj4dabnj8a4w.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:14Z",
      "native_id": "2611.03155",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03155"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Minimax regret and monotone optimality in multiple testing",
  "title_text": "Minimax regret and monotone optimality in multiple testing",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/1zj4dabnj8a4w",
  "venue": "arXiv",
  "versions": []
}