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Bayesian Group Testing Regression with a Shape-Free Dilution Curve — arXiv:2609.08439

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08439
License (as archived): CC0-1.0
Archived: 2026-10-16T14:09:52.287Z
SHA-256 of the archived text: 5a3f992671e90456c89fedafd9a9af08c8b66bcec43a60a50aa4e5b0b57844be
Stable URL of this page: https://metraipolis.com/documento/dc_ef057fbdd7ed

How to cite

Bayesian Group Testing Regression with a Shape-Free Dilution Curve — arXiv:2609.08439 — arXiv (en), https://arxiv.org/abs/2609.08439 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ef057fbdd7ed (Metraipolis archive dc_ef057fbdd7ed, sha256 5a3f992671e90456c89fedafd9a9af08c8b66bcec43a60a50aa4e5b0b57844be)

BibTeX

@misc{dcef057fbdd7ed,
  title = {Bayesian Group Testing Regression with a Shape-Free Dilution Curve — arXiv:2609.08439},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08439},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ef057fbdd7ed; sha256 5a3f992671e90456c89fedafd9a9af08c8b66bcec43a60a50aa4e5b0b57844be},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08439. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Group testing pools specimens to cut the cost of screening, but pooling positive specimens with negative ones lowers assay sensitivity, so that the sensitivity of a pooled test depends on how many of its members are positive. Regression models for group testing accommodate this dilution effect through submodels that fix a one-parameter shape for that dependence. We propose BADGER (Bayesian Analysis of Dilution in Group tEsting Regression), a regression model incorporating a shape-free dilution curve. The pooled sensitivity is modeled as a nondecreasing function represented by nonnegative increments with a Dirichlet prior, so that the parametric submodels become special cases. By an appropriate augmentation, every full conditional of BADGER is in closed form and inference is carried out by an exact Gibbs sampler. The evidence for dilution is measured by a Bayes factor computed from the posterior draws. We validate the BADGER model through simulation studies across different dilution shapes, prevalences and pool sizes. We further illustrate the method on hepatitis E serology from a national health survey.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Computation (stat.CO).
Pagina del lavoro: https://arxiv.org/abs/2609.08439 — 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 2026-10-16T13:59:27.904Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).