{
  "abstract": "We studied simultaneous recovery of a latent group count, subject partition, and signature means from multiview ordinal ranks. A normalized reference rearrangement score is an affine Spearman correlation; the problem concerns the information retained by several such scores and their noise geometry. We develop an ordered-gap estimator with direction-specific concentration radii. Its finite-sample guarantee separates signature noise, missing groups, and candidate coverage, and allows the group count to grow under an attainable-margin condition. A coordinatewise partition followed by label-tuple refinement recovers groups even when every individual coordinate has population collisions. A binary-code construction explains why the one-dimensional inverse-group margin restriction need not govern a retained coordinate bank. We derive actual-law concentration from bounded ranking modules and a fixed-dimensional combinatorial central limit theorem under an observed permutation representation. A binary ordinal submodel gives logarithmic exact-recovery information scales. A subject-effect construction distinguishes persistent overlap from reducible measurement noise, and an additional ordinal anchor quantifies how new information lowers that error floor. Five focused simulation scenarios illustrate direction-specific calibration, growing-group recovery, coordinate collision resolution, variance reduction, and the effect of an additional ordinal anchor.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.38986",
  "author_total": 4,
  "authors": [
    {
      "name": "Fanghua Chen"
    },
    {
      "name": "Yizhou Cai"
    },
    {
      "name": "Ting Fung Ma"
    },
    {
      "name": "Yun Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0zhy897sba08y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.38986",
  "fulltext_url": null,
  "id": "0zhy897sba08y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.38986",
  "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/2610.38986",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0zhy897sba08y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:32Z",
      "native_id": "2610.38986",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.38986"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Noise Calibration and Exact Recovery for Multiview Ordinal Rearrangement Data",
  "title_text": "Noise Calibration and Exact Recovery for Multiview Ordinal Rearrangement Data",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/0zhy897sba08y",
  "venue": "arXiv",
  "versions": []
}