{
  "abstract": "Influential Outlier Detection is developed for mixed-effects models on clustered data. The Influential Outlier Metric is defined as a combination of SHapley Additive exPlanantion (SHAP) values and model residuals, both of which undergo a change of measure transformation. Building on previous work showcasing the suitability of using Normalizing flows to map arbitrary distributions to a flexible base distribution for statistical inference, the Normalizing Flows are constructed to allows contextual information and also provide a goodness of fit diagnostic for model evaluation. The use of SHAP values in the construction moves away from model specific tools and instead provides point-wise model agnostic influential outlier. The advantages and limitations of this approach are examined in several models including the linear model, the random forest, and gradient-boosted trees.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.01720",
  "author_total": 3,
  "authors": [
    {
      "name": "Colin C Jones"
    },
    {
      "name": "David A Campbell"
    },
    {
      "name": "Yan Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a21vzrbrg0209.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.01720",
  "fulltext_url": null,
  "id": "a21vzrbrg0209",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.01720",
  "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.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Statistical Methods and Models"
    ]
  ],
  "openalex": "W7216377678",
  "pdf_url": "https://arxiv.org/pdf/2610.01720",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a21vzrbrg0209.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:14Z",
      "native_id": "2610.01720",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.01720"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Model-Agnostic Influential Outlier Detection for Mixed Effects and Multi-Level Models",
  "title_text": "Model-Agnostic Influential Outlier Detection for Mixed Effects and Multi-Level Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:14Z",
  "url": "https://research.draconitic.com/paper/a21vzrbrg0209",
  "venue": "arXiv",
  "versions": []
}