{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:72PM3OBYLVE4YYT2KHSEY73W4I","short_pith_number":"pith:72PM3OBY","schema_version":"1.0","canonical_sha256":"fe9ecdb8385d49cc627a51e44c7f76e2194b8024d1bbc0af46f75a2a093423cf","source":{"kind":"arxiv","id":"2606.05072","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Sequential Change Detection using Mixtures of Predictive Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"H. Vincent Poor, Topi Halme, Visa Koivunen","submitted_at":"2026-06-03T16:30:47Z","abstract_excerpt":"This paper studies the problem of detecting a change in the distribution of a sequence of independent observations when the post-change distribution is unknown. We propose a novel change detection algorithm, termed Predictive-Mixture CuSum (PM-CuSum), which combines predictive distributions constructed from sliding windows of different lengths within a CuSum recursion. The predictive distributions are aggregated using adaptive weights based on their recent predictive performance. We show that PM-CuSum achieves first-order asymptotic optimality under mild conditions, and that its asymptotic del"},"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":"2606.05072","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-06-03T16:30:47Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"2c6ddc968806f76384c15f667b8c6513c1e1fdf85c174225d91ad2eb402ffb27","abstract_canon_sha256":"003e6e817ee225a46040420442c3c82befdcec88e40bf685c319fa3e23193d44"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:10:05.056374Z","signature_b64":"aowO/DIhwSst5OoP1Zz526Kmsc9QJSI1cJ5XvabZhY5AqX0fbGh5v95/8yWScAA1UBSCkDgWoWAt3k1qnhkPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe9ecdb8385d49cc627a51e44c7f76e2194b8024d1bbc0af46f75a2a093423cf","last_reissued_at":"2026-06-04T01:10:05.055882Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:10:05.055882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Sequential Change Detection using Mixtures of Predictive Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"H. Vincent Poor, Topi Halme, Visa Koivunen","submitted_at":"2026-06-03T16:30:47Z","abstract_excerpt":"This paper studies the problem of detecting a change in the distribution of a sequence of independent observations when the post-change distribution is unknown. We propose a novel change detection algorithm, termed Predictive-Mixture CuSum (PM-CuSum), which combines predictive distributions constructed from sliding windows of different lengths within a CuSum recursion. The predictive distributions are aggregated using adaptive weights based on their recent predictive performance. We show that PM-CuSum achieves first-order asymptotic optimality under mild conditions, and that its asymptotic del"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05072","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/2606.05072/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":"2606.05072","created_at":"2026-06-04T01:10:05.055955+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05072v1","created_at":"2026-06-04T01:10:05.055955+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05072","created_at":"2026-06-04T01:10:05.055955+00:00"},{"alias_kind":"pith_short_12","alias_value":"72PM3OBYLVE4","created_at":"2026-06-04T01:10:05.055955+00:00"},{"alias_kind":"pith_short_16","alias_value":"72PM3OBYLVE4YYT2","created_at":"2026-06-04T01:10:05.055955+00:00"},{"alias_kind":"pith_short_8","alias_value":"72PM3OBY","created_at":"2026-06-04T01:10:05.055955+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/72PM3OBYLVE4YYT2KHSEY73W4I","json":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I.json","graph_json":"https://pith.science/api/pith-number/72PM3OBYLVE4YYT2KHSEY73W4I/graph.json","events_json":"https://pith.science/api/pith-number/72PM3OBYLVE4YYT2KHSEY73W4I/events.json","paper":"https://pith.science/paper/72PM3OBY"},"agent_actions":{"view_html":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I","download_json":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I.json","view_paper":"https://pith.science/paper/72PM3OBY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05072&json=true","fetch_graph":"https://pith.science/api/pith-number/72PM3OBYLVE4YYT2KHSEY73W4I/graph.json","fetch_events":"https://pith.science/api/pith-number/72PM3OBYLVE4YYT2KHSEY73W4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I/action/storage_attestation","attest_author":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I/action/author_attestation","sign_citation":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I/action/citation_signature","submit_replication":"https://pith.science/pith/72PM3OBYLVE4YYT2KHSEY73W4I/action/replication_record"}},"created_at":"2026-06-04T01:10:05.055955+00:00","updated_at":"2026-06-04T01:10:05.055955+00:00"}