{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DVITGJLQTVX35FVQVI4V7AHG42","short_pith_number":"pith:DVITGJLQ","schema_version":"1.0","canonical_sha256":"1d513325709d6fbe96b0aa395f80e6e69fef5992745b9026dd61cee12b343297","source":{"kind":"arxiv","id":"2507.02552","version":4},"attestation_state":"computed","paper":{"title":"Covariance scanning for adaptively optimal change point detection in high-dimensional linear models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Haeran Cho, Housen Li","submitted_at":"2025-07-03T11:53:31Z","abstract_excerpt":"This paper investigates the detection and estimation of a single change in high-dimensional linear models. We derive minimax lower bounds for the detection boundary and the estimation rate, which uncover a phase transition governed by the sparsity of the covariance-weighted differential parameter. This form of \"inherent sparsity\" captures a delicate interplay between the covariance structure of the regressors and the change in regression coefficients on the detectability of a change point. Complementing the lower bounds, we introduce two covariance scanning-based methods, McScan and QcSan, whi"},"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":"2507.02552","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-07-03T11:53:31Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"7d6d6e9baf64319e2c287ff02da55db920b106db6ca5d9e7f68f03e56986645e","abstract_canon_sha256":"88597b5e4abd8fe0ec1eb0ee9fcbe8d6e4b731e81caa94d9a0aec1790c0749af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:04:33.278102Z","signature_b64":"ZYnyKejpS+ncVTE/E+ukr/oY90iL+DIcLSyxUtq2FIbYChYGkBG8FpRVMsogX4NN7jEbLosYPjnfrVCdI0vSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d513325709d6fbe96b0aa395f80e6e69fef5992745b9026dd61cee12b343297","last_reissued_at":"2026-06-02T03:04:33.277615Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:04:33.277615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Covariance scanning for adaptively optimal change point detection in high-dimensional linear models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Haeran Cho, Housen Li","submitted_at":"2025-07-03T11:53:31Z","abstract_excerpt":"This paper investigates the detection and estimation of a single change in high-dimensional linear models. We derive minimax lower bounds for the detection boundary and the estimation rate, which uncover a phase transition governed by the sparsity of the covariance-weighted differential parameter. This form of \"inherent sparsity\" captures a delicate interplay between the covariance structure of the regressors and the change in regression coefficients on the detectability of a change point. Complementing the lower bounds, we introduce two covariance scanning-based methods, McScan and QcSan, whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02552","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/2507.02552/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":"2507.02552","created_at":"2026-06-02T03:04:33.277678+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.02552v4","created_at":"2026-06-02T03:04:33.277678+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02552","created_at":"2026-06-02T03:04:33.277678+00:00"},{"alias_kind":"pith_short_12","alias_value":"DVITGJLQTVX3","created_at":"2026-06-02T03:04:33.277678+00:00"},{"alias_kind":"pith_short_16","alias_value":"DVITGJLQTVX35FVQ","created_at":"2026-06-02T03:04:33.277678+00:00"},{"alias_kind":"pith_short_8","alias_value":"DVITGJLQ","created_at":"2026-06-02T03:04:33.277678+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/DVITGJLQTVX35FVQVI4V7AHG42","json":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42.json","graph_json":"https://pith.science/api/pith-number/DVITGJLQTVX35FVQVI4V7AHG42/graph.json","events_json":"https://pith.science/api/pith-number/DVITGJLQTVX35FVQVI4V7AHG42/events.json","paper":"https://pith.science/paper/DVITGJLQ"},"agent_actions":{"view_html":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42","download_json":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42.json","view_paper":"https://pith.science/paper/DVITGJLQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.02552&json=true","fetch_graph":"https://pith.science/api/pith-number/DVITGJLQTVX35FVQVI4V7AHG42/graph.json","fetch_events":"https://pith.science/api/pith-number/DVITGJLQTVX35FVQVI4V7AHG42/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42/action/storage_attestation","attest_author":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42/action/author_attestation","sign_citation":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42/action/citation_signature","submit_replication":"https://pith.science/pith/DVITGJLQTVX35FVQVI4V7AHG42/action/replication_record"}},"created_at":"2026-06-02T03:04:33.277678+00:00","updated_at":"2026-06-02T03:04:33.277678+00:00"}