{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:W63R4XX2CNDODGV5CV6I6X4LT6","short_pith_number":"pith:W63R4XX2","schema_version":"1.0","canonical_sha256":"b7b71e5efa1346e19abd157c8f5f8b9fbde8d403560d07733701833e0b757963","source":{"kind":"arxiv","id":"2101.06839","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Change Point Monitoring for High-Dimensional Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Hao Yan, Runmin Wang, Teng Wu, Xiaofeng Shao","submitted_at":"2021-01-18T02:10:28Z","abstract_excerpt":"In this paper, we propose a class of monitoring statistics for a mean shift in a sequence of high-dimensional observations. Inspired by the recent U-statistic based retrospective tests developed by Wang et al.(2019) and Zhang et al.(2020), we advance the U-statistic based approach to the sequential monitoring problem by developing a new adaptive monitoring procedure that can detect both dense and sparse changes in real-time. Unlike Wang et al.(2019) and Zhang et al.(2020), where self-normalization was used in their tests, we instead introduce a class of estimators for $q$-norm of the covarianc"},"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":"2101.06839","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2021-01-18T02:10:28Z","cross_cats_sorted":[],"title_canon_sha256":"5affccaf81722dcdba0f7a91177d738e094cc15927976a98d19dcbdf05e00ac9","abstract_canon_sha256":"ed7422dab760e013f8b61a116224092065a305e99e76a771810c164820123fbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:07:37.655189Z","signature_b64":"DiDZG3+K3D8gsEPD8IatjaMqgzk0VFiX4pYGFPXYGeZXPQVPfpjak+K+/pIEoL1dunMwkf8hXmaVxtZpn+PRAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7b71e5efa1346e19abd157c8f5f8b9fbde8d403560d07733701833e0b757963","last_reissued_at":"2026-07-05T02:07:37.654715Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:07:37.654715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Change Point Monitoring for High-Dimensional Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Hao Yan, Runmin Wang, Teng Wu, Xiaofeng Shao","submitted_at":"2021-01-18T02:10:28Z","abstract_excerpt":"In this paper, we propose a class of monitoring statistics for a mean shift in a sequence of high-dimensional observations. Inspired by the recent U-statistic based retrospective tests developed by Wang et al.(2019) and Zhang et al.(2020), we advance the U-statistic based approach to the sequential monitoring problem by developing a new adaptive monitoring procedure that can detect both dense and sparse changes in real-time. Unlike Wang et al.(2019) and Zhang et al.(2020), where self-normalization was used in their tests, we instead introduce a class of estimators for $q$-norm of the covarianc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.06839","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2101.06839/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":"2101.06839","created_at":"2026-07-05T02:07:37.654765+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.06839v1","created_at":"2026-07-05T02:07:37.654765+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.06839","created_at":"2026-07-05T02:07:37.654765+00:00"},{"alias_kind":"pith_short_12","alias_value":"W63R4XX2CNDO","created_at":"2026-07-05T02:07:37.654765+00:00"},{"alias_kind":"pith_short_16","alias_value":"W63R4XX2CNDODGV5","created_at":"2026-07-05T02:07:37.654765+00:00"},{"alias_kind":"pith_short_8","alias_value":"W63R4XX2","created_at":"2026-07-05T02:07:37.654765+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6","json":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6.json","graph_json":"https://pith.science/api/pith-number/W63R4XX2CNDODGV5CV6I6X4LT6/graph.json","events_json":"https://pith.science/api/pith-number/W63R4XX2CNDODGV5CV6I6X4LT6/events.json","paper":"https://pith.science/paper/W63R4XX2"},"agent_actions":{"view_html":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6","download_json":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6.json","view_paper":"https://pith.science/paper/W63R4XX2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.06839&json=true","fetch_graph":"https://pith.science/api/pith-number/W63R4XX2CNDODGV5CV6I6X4LT6/graph.json","fetch_events":"https://pith.science/api/pith-number/W63R4XX2CNDODGV5CV6I6X4LT6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6/action/storage_attestation","attest_author":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6/action/author_attestation","sign_citation":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6/action/citation_signature","submit_replication":"https://pith.science/pith/W63R4XX2CNDODGV5CV6I6X4LT6/action/replication_record"}},"created_at":"2026-07-05T02:07:37.654765+00:00","updated_at":"2026-07-05T02:07:37.654765+00:00"}