{"paper":{"title":"On robust stopping times for detecting changes in distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Mher Safarian, Yuri Golubev","submitted_at":"2018-04-24T13:27:44Z","abstract_excerpt":"Let $X_1,X_2,\\ldots $ be independent random variables observed sequentially and such that $X_1,\\ldots,X_{\\theta-1}$ have a common probability density $p_0$, while $X_\\theta,X_{\\theta+1},\\ldots $ are all distributed according to $p_1\\neq p_0$. It is assumed that $p_0$ and $p_1$ are known, but the time change $\\theta\\in \\mathbb{Z}^+$ is unknown and the goal is to construct a stopping time $\\tau$ that detects the change-point $\\theta$ as soon as possible. The existing approaches to this problem rely essentially on some a priori information about $\\theta$. For instance, in Bayes approaches, it is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.09014","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}