2026-07-26T04:38:47Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:15488 2024-03-06T10:55:25Z ddc:004 open_access

Identity Testing Under Label Mismatch Canonne, Clément L. Wimmer, Karl distribution testing property testing permutations lower bounds Testing whether the observed data conforms to a purported model (probability distribution) is a basic and fundamental statistical task, and one that is by now well understood. However, the standard formulation, identity testing, fails to capture many settings of interest; in this work, we focus on one such natural setting, identity testing under promise of permutation. In this setting, the unknown distribution is assumed to be equal to the purported one, up to a relabeling (permutation) of the model: however, due to a systematic error in the reporting of the data, this relabeling may not be the identity. The goal is then to test identity under this assumption: equivalently, whether this systematic labeling error led to a data distribution statistically far from the reference model. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Clément L. Canonne and Karl Wimmer 2021 Is Part Of LIPIcs, Volume 212, 32nd International Symposium on Algorithms and Computation (ISAAC 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ISAAC.2021.55 urn:nbn:de:0030-drops-154880 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2021.55 eng https://creativecommons.org/licenses/by/4.0/legalcode