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Observation-Specific Uncertainty Modeling for Latent-Variable Regression in High-Dimensional Analytical Data — arXiv:2610.41815

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41815
License (as archived): CC0-1.0
Archived: 2026-11-02T07:56:07.247Z
SHA-256 of the archived text: 91c5901dd7f1d8f4bab70d1e91960a7a161a7c3e3f7598abe5cd188f4142edbe
Stable URL of this page: https://metraipolis.com/documento/dc_7bb0ec363dca

How to cite

Observation-Specific Uncertainty Modeling for Latent-Variable Regression in High-Dimensional Analytical Data — arXiv:2610.41815 — arXiv (en), https://arxiv.org/abs/2610.41815 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7bb0ec363dca (Metraipolis archive dc_7bb0ec363dca, sha256 91c5901dd7f1d8f4bab70d1e91960a7a161a7c3e3f7598abe5cd188f4142edbe)

BibTeX

@misc{dc7bb0ec363dca,
  title = {Observation-Specific Uncertainty Modeling for Latent-Variable Regression in High-Dimensional Analytical Data — arXiv:2610.41815},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41815},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7bb0ec363dca; sha256 91c5901dd7f1d8f4bab70d1e91960a7a161a7c3e3f7598abe5cd188f4142edbe},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41815. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2610.41815 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:41:00.705Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).