{
  "abstract": "We consider quickest change-point detection in generalized linear models when the post-change parameter is unknown. Such problems arise, for example, when monitoring whether the relationship between a learned representation and an observed response changes over time. Since detection must occur soon after a change, the post-change parameter must be estimated from a limited number of observations, making finite-sample estimation accuracy particularly important. We propose a window-limited CUSUM procedure that estimates the post-change parameter using a variational-inequality (VI) formulation. For noncanonical links, VI estimation can offer favorable finite-sample accuracy over short windows despite the asymptotic efficiency advantage of maximum likelihood estimation. We characterize how estimation error reduces the effective post-change information rate and affects detection delay, and derive finite-window guarantees accounting for both statistical error and incomplete numerical optimization. The resulting procedure retains false-alarm control and achieves first-order asymptotic optimality under suitable conditions. Numerical experiments compare detection delay and update cost for fixed-budget estimation and benchmark VI-WLCUSUM against alternative detectors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16616",
  "author_total": 2,
  "authors": [
    {
      "name": "Jonghyeok Lee"
    },
    {
      "name": "Yao Xie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3cnm6kkce1p0t.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16616",
  "fulltext_url": null,
  "id": "3cnm6kkce1p0t",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16616",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1804|Advanced Statistical Process Monitoring"
    ]
  ],
  "openalex": "W7221806565",
  "pdf_url": "https://arxiv.org/pdf/2610.16616",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3cnm6kkce1p0t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2610.16616",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16616"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Quickest Change-point Detection in Generalized Linear Models with Variational Estimation",
  "title_text": "Quickest Change-point Detection in Generalized Linear Models with Variational Estimation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/3cnm6kkce1p0t",
  "venue": "arXiv",
  "versions": []
}