{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BMU4GVMJSCCWWTNINJNX5TOTF3","short_pith_number":"pith:BMU4GVMJ","schema_version":"1.0","canonical_sha256":"0b29c3558990856b4da86a5b7ecdd32edf175fd72da8a146ebc88530c4269864","source":{"kind":"arxiv","id":"2203.03532","version":4},"attestation_state":"computed","paper":{"title":"E-detectors: a nonparametric framework for sequential change detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Aaditya Ramdas, Alessandro Rinaldo, Jaehyeok Shin","submitted_at":"2022-03-07T17:25:02Z","abstract_excerpt":"Sequential change detection is a classical problem with a variety of applications. However, the majority of prior work has been parametric, for example, focusing on exponential families. We develop a fundamentally new and general framework for sequential change detection when the pre- and post-change distributions are nonparametrically specified (and thus composite). Our procedures come with clean, nonasymptotic bounds on the average run length (frequency of false alarms). In certain nonparametric cases (like sub-Gaussian or sub-exponential), we also provide near-optimal bounds on the detectio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2203.03532","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-03-07T17:25:02Z","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"title_canon_sha256":"a0e8a77d32c1216d0cb1df8c297e7c3108348ccc2c3650bcd139bada60babff7","abstract_canon_sha256":"a1f6e08d71e3724a5d1e43a7e2f743458577a2a2d85c276132f93fecdd642dde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:18.149311Z","signature_b64":"b0ROruAqeBBpJaFZHR2wRaBBqS7LsGeXvtv3lGluN6hkSsjey84nCOpWW9NOaqzRNSaIpmkmrBOwH+C5lmMQAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b29c3558990856b4da86a5b7ecdd32edf175fd72da8a146ebc88530c4269864","last_reissued_at":"2026-07-05T07:06:18.148895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:18.148895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"E-detectors: a nonparametric framework for sequential change detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Aaditya Ramdas, Alessandro Rinaldo, Jaehyeok Shin","submitted_at":"2022-03-07T17:25:02Z","abstract_excerpt":"Sequential change detection is a classical problem with a variety of applications. However, the majority of prior work has been parametric, for example, focusing on exponential families. We develop a fundamentally new and general framework for sequential change detection when the pre- and post-change distributions are nonparametrically specified (and thus composite). Our procedures come with clean, nonasymptotic bounds on the average run length (frequency of false alarms). In certain nonparametric cases (like sub-Gaussian or sub-exponential), we also provide near-optimal bounds on the detectio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.03532","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.03532/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2203.03532","created_at":"2026-07-05T07:06:18.148954+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.03532v4","created_at":"2026-07-05T07:06:18.148954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.03532","created_at":"2026-07-05T07:06:18.148954+00:00"},{"alias_kind":"pith_short_12","alias_value":"BMU4GVMJSCCW","created_at":"2026-07-05T07:06:18.148954+00:00"},{"alias_kind":"pith_short_16","alias_value":"BMU4GVMJSCCWWTNI","created_at":"2026-07-05T07:06:18.148954+00:00"},{"alias_kind":"pith_short_8","alias_value":"BMU4GVMJ","created_at":"2026-07-05T07:06:18.148954+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29290","citing_title":"SWORD: Spectral Wasserstein Online Regime Detection in Dynamic Networks","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3","json":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3.json","graph_json":"https://pith.science/api/pith-number/BMU4GVMJSCCWWTNINJNX5TOTF3/graph.json","events_json":"https://pith.science/api/pith-number/BMU4GVMJSCCWWTNINJNX5TOTF3/events.json","paper":"https://pith.science/paper/BMU4GVMJ"},"agent_actions":{"view_html":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3","download_json":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3.json","view_paper":"https://pith.science/paper/BMU4GVMJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.03532&json=true","fetch_graph":"https://pith.science/api/pith-number/BMU4GVMJSCCWWTNINJNX5TOTF3/graph.json","fetch_events":"https://pith.science/api/pith-number/BMU4GVMJSCCWWTNINJNX5TOTF3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3/action/storage_attestation","attest_author":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3/action/author_attestation","sign_citation":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3/action/citation_signature","submit_replication":"https://pith.science/pith/BMU4GVMJSCCWWTNINJNX5TOTF3/action/replication_record"}},"created_at":"2026-07-05T07:06:18.148954+00:00","updated_at":"2026-07-05T07:06:18.148954+00:00"}