{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ETNETRBWB2I3V2UVUEW3HNFMHQ","short_pith_number":"pith:ETNETRBW","schema_version":"1.0","canonical_sha256":"24da49c4360e91baea95a12db3b4ac3c0af8b1791e353b1e3840f5ab6eebd065","source":{"kind":"arxiv","id":"2506.01452","version":2},"attestation_state":"computed","paper":{"title":"e-GAI: e-value-based Generalized $\\alpha$-Investing for Online False Discovery Rate Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Changliang Zou, Haojie Ren, Yifan Zhang, Zijian Wei","submitted_at":"2025-06-02T09:08:52Z","abstract_excerpt":"Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art generalized $\\alpha$-investing (GAI) algorithms can control online false discovery rate (FDR) on p-values only under specific dependence structures, a situation that rarely occurs in practice. The e-LOND algorithm (Xu & Ramdas, 2024) utilizes e-values to achieve online FDR control under arbitrary dependence but suffers from a significant loss in power as testing levels are derived from pre-specified descent sequences. T"},"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":"2506.01452","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-06-02T09:08:52Z","cross_cats_sorted":[],"title_canon_sha256":"b75ab7336d446450914e12ac22cff2d98d345b903e4e5120490a5636666b3101","abstract_canon_sha256":"42202e5cbacb9785e97f566da9abaf06158da9914b2c6c5d315935d54425f910"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:04.414691Z","signature_b64":"tBGUWLz+d9LzdMTa6j9MNZsEoi7it3xDVZ6eqoJtY9WMi2OqzOGKjItPV4AqtCVcTAk2hckVeF6WxBJco7UBDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24da49c4360e91baea95a12db3b4ac3c0af8b1791e353b1e3840f5ab6eebd065","last_reissued_at":"2026-07-05T11:48:04.414135Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:04.414135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"e-GAI: e-value-based Generalized $\\alpha$-Investing for Online False Discovery Rate Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Changliang Zou, Haojie Ren, Yifan Zhang, Zijian Wei","submitted_at":"2025-06-02T09:08:52Z","abstract_excerpt":"Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art generalized $\\alpha$-investing (GAI) algorithms can control online false discovery rate (FDR) on p-values only under specific dependence structures, a situation that rarely occurs in practice. The e-LOND algorithm (Xu & Ramdas, 2024) utilizes e-values to achieve online FDR control under arbitrary dependence but suffers from a significant loss in power as testing levels are derived from pre-specified descent sequences. T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01452","kind":"arxiv","version":2},"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/2506.01452/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":"2506.01452","created_at":"2026-07-05T11:48:04.414197+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.01452v2","created_at":"2026-07-05T11:48:04.414197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01452","created_at":"2026-07-05T11:48:04.414197+00:00"},{"alias_kind":"pith_short_12","alias_value":"ETNETRBWB2I3","created_at":"2026-07-05T11:48:04.414197+00:00"},{"alias_kind":"pith_short_16","alias_value":"ETNETRBWB2I3V2UV","created_at":"2026-07-05T11:48:04.414197+00:00"},{"alias_kind":"pith_short_8","alias_value":"ETNETRBW","created_at":"2026-07-05T11:48:04.414197+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/ETNETRBWB2I3V2UVUEW3HNFMHQ","json":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ.json","graph_json":"https://pith.science/api/pith-number/ETNETRBWB2I3V2UVUEW3HNFMHQ/graph.json","events_json":"https://pith.science/api/pith-number/ETNETRBWB2I3V2UVUEW3HNFMHQ/events.json","paper":"https://pith.science/paper/ETNETRBW"},"agent_actions":{"view_html":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ","download_json":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ.json","view_paper":"https://pith.science/paper/ETNETRBW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.01452&json=true","fetch_graph":"https://pith.science/api/pith-number/ETNETRBWB2I3V2UVUEW3HNFMHQ/graph.json","fetch_events":"https://pith.science/api/pith-number/ETNETRBWB2I3V2UVUEW3HNFMHQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ/action/storage_attestation","attest_author":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ/action/author_attestation","sign_citation":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ/action/citation_signature","submit_replication":"https://pith.science/pith/ETNETRBWB2I3V2UVUEW3HNFMHQ/action/replication_record"}},"created_at":"2026-07-05T11:48:04.414197+00:00","updated_at":"2026-07-05T11:48:04.414197+00:00"}