{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:14:31Z","timestamp":1790759671819,"version":"4.1.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>The contamination detection problem aims to determine whether a set of observations has been contaminated, i.e., whether it contains points drawn from a distribution different from the reference distribution. Here, we consider a supervised problem, where labelled samples drawn from both the reference distribution and the contamination distribution are available at training time. This problem is motivated by the detection of rare cells in flow cytometry. Compared to novelty detection problems or two-sample testing, where only samples from the reference distribution are available, the challenge lies in efficiently leveraging the observations from the contamination detection to design more powerful tests. In this article, we introduce a test for the supervised contamination detection problem. We provide non-asymptotic guarantees on its Type-I error, and characterize its detection rate. The test relies on estimating reference and contamination densities using histograms, and its power depends strongly on the choice of the corresponding partition. We present an algorithm for judiciously choosing the partition that results in a powerful test. Simulations illustrate the good empirical performances of our partition selection algorithm and the efficiency of our test. Finally, we showcase our method and apply it to a real flow cytometry dataset.<\/jats:p>","DOI":"10.1093\/biomet\/asaf063","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:44:53Z","timestamp":1755863093000},"source":"Crossref","is-referenced-by-count":1,"title":["Supervised contamination detection, with application to flow cytometry data"],"prefix":"10.1093","volume":"112","author":[{"given":"S","family":"Gaucher","sequence":"first","affiliation":[{"name":"CMAP, \u00c9cole Polytechnique, IP Paris, Route de Saclay , 91120 Palaiseau,","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G","family":"Blanchard","sequence":"additional","affiliation":[{"name":"CNRS, INRIA, Laboratoire de Math\u00e9matiques d\u2019Orsay, Universit\u00e9 Paris-Saclay , 307 rue Michel Magat , 91405 Orsay, \u00a0 gilles.blanchard@universite-paris-saclay.fr \u2003 frederic.chazal@inria.fr","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F","family":"Chazal","sequence":"additional","affiliation":[{"name":"CNRS, INRIA, Laboratoire de Math\u00e9matiques d\u2019Orsay, Universit\u00e9 Paris-Saclay , 307 rue Michel Magat , 91405 Orsay, \u00a0 gilles.blanchard@universite-paris-saclay.fr \u2003 frederic.chazal@inria.fr","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"2026032607103503500_asaf063-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-319-47578-3","volume-title":"Outlier Analysis","author":"Aggarwal","year":"2017"},{"key":"2026032607103503500_asaf063-B2","first-page":"222","author":"Alexandari","year":"2020"},{"key":"2026032607103503500_asaf063-B3","first-page":"2973","article-title":"Semi-supervised novelty detection","volume":"11","author":"Blanchard","year":"2010","journal-title":"J. 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