{
  "abstract": "Across many fields, including medical diagnostics, process monitoring, and quality control, decisions often rely on point predictions for single samples. Observation-specific uncertainty is needed to assess the risk of decision error when predictions are compared with thresholds. Latent-variable regression is widely used for such predictions, but observation-specific uncertainty remains difficult to estimate. Traditional methods assume unbiased predictions, attributing prediction uncertainty solely to variance. However, dimensionality reduction can introduce observation-specific bias, violating this assumption. We introduce a framework using Gaussian processes trained on latent scores and prediction errors to estimate observation-specific bias and error variance, separating systematic error from epistemic and aleatoric variance. In Raman measurements of a fructose-sucrose model system, estimated prediction-error distributions agreed with empirical distributions; bias correction reduced mean squared error, and decision limits incorporating bias and variance improved analyte detection over variance-only limits. An aqueous-solubility application using molecular descriptors demonstrated the framework's generalisation beyond spectroscopy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41815",
  "author_total": 5,
  "authors": [
    {
      "name": "Carl Emil Eskildsen"
    },
    {
      "name": "Georgios Papadopoulos"
    },
    {
      "name": "Kevin A. Giraldo"
    },
    {
      "name": "Mauricio Barahona"
    },
    {
      "name": "Molly M. Stevens"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fhmrjvcg66jm9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41815",
  "fulltext_url": null,
  "id": "fhmrjvcg66jm9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41815",
  "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"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41815",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fhmrjvcg66jm9.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:25Z",
      "native_id": "2610.41815",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41815"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Observation-Specific Uncertainty Modeling for Latent-Variable Regression in High-Dimensional Analytical Data",
  "title_text": "Observation-Specific Uncertainty Modeling for Latent-Variable Regression in High-Dimensional Analytical Data",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/fhmrjvcg66jm9",
  "venue": "arXiv",
  "versions": []
}