{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YNYBFWOAVJDVJNQ2GFW3MEIDE5","short_pith_number":"pith:YNYBFWOA","schema_version":"1.0","canonical_sha256":"c37012d9c0aa4754b61a316db61103275065f05d375d73fd8816d59fcad1fc2b","source":{"kind":"arxiv","id":"2109.15287","version":1},"attestation_state":"computed","paper":{"title":"Power-enhanced simultaneous test of high-dimensional mean vectors and covariance matrices with application to gene-set testing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Danning Li, Lingzhou Xue, Runze Li, Xiufan Yu","submitted_at":"2021-09-30T17:31:46Z","abstract_excerpt":"Power-enhanced tests with high-dimensional data have received growing attention in theoretical and applied statistics in recent years. Existing tests possess their respective high-power regions, and we may lack prior knowledge about the alternatives when testing for a problem of interest in practice. There is a critical need of developing powerful testing procedures against more general alternatives. This paper studies the joint test of two-sample mean vectors and covariance matrices for high-dimensional data. We first expand the high-power region of high-dimensional mean tests or covariance 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":"2109.15287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2021-09-30T17:31:46Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"d3d7b85554748d4ce47007f0a4009649e81cfb611aa6870df6fd97ff8142d1f7","abstract_canon_sha256":"8d8a92250c405243d1d44f9993587907618615faa94027f3703fbdd27763519a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:18:57.317494Z","signature_b64":"NB4uk4dKtsiVcR5eC8h5QIOPDcDbjw9w96Q0JddUPghn4KpS8lPdLsgjEnLbUrI33LbTtGA2cNsvjEVE6V8fBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c37012d9c0aa4754b61a316db61103275065f05d375d73fd8816d59fcad1fc2b","last_reissued_at":"2026-07-05T03:18:57.317001Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:18:57.317001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Power-enhanced simultaneous test of high-dimensional mean vectors and covariance matrices with application to gene-set testing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Danning Li, Lingzhou Xue, Runze Li, Xiufan Yu","submitted_at":"2021-09-30T17:31:46Z","abstract_excerpt":"Power-enhanced tests with high-dimensional data have received growing attention in theoretical and applied statistics in recent years. Existing tests possess their respective high-power regions, and we may lack prior knowledge about the alternatives when testing for a problem of interest in practice. There is a critical need of developing powerful testing procedures against more general alternatives. This paper studies the joint test of two-sample mean vectors and covariance matrices for high-dimensional data. We first expand the high-power region of high-dimensional mean tests or covariance t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.15287","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/2109.15287/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":"2109.15287","created_at":"2026-07-05T03:18:57.317064+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.15287v1","created_at":"2026-07-05T03:18:57.317064+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.15287","created_at":"2026-07-05T03:18:57.317064+00:00"},{"alias_kind":"pith_short_12","alias_value":"YNYBFWOAVJDV","created_at":"2026-07-05T03:18:57.317064+00:00"},{"alias_kind":"pith_short_16","alias_value":"YNYBFWOAVJDVJNQ2","created_at":"2026-07-05T03:18:57.317064+00:00"},{"alias_kind":"pith_short_8","alias_value":"YNYBFWOA","created_at":"2026-07-05T03:18:57.317064+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/YNYBFWOAVJDVJNQ2GFW3MEIDE5","json":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5.json","graph_json":"https://pith.science/api/pith-number/YNYBFWOAVJDVJNQ2GFW3MEIDE5/graph.json","events_json":"https://pith.science/api/pith-number/YNYBFWOAVJDVJNQ2GFW3MEIDE5/events.json","paper":"https://pith.science/paper/YNYBFWOA"},"agent_actions":{"view_html":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5","download_json":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5.json","view_paper":"https://pith.science/paper/YNYBFWOA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.15287&json=true","fetch_graph":"https://pith.science/api/pith-number/YNYBFWOAVJDVJNQ2GFW3MEIDE5/graph.json","fetch_events":"https://pith.science/api/pith-number/YNYBFWOAVJDVJNQ2GFW3MEIDE5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5/action/storage_attestation","attest_author":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5/action/author_attestation","sign_citation":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5/action/citation_signature","submit_replication":"https://pith.science/pith/YNYBFWOAVJDVJNQ2GFW3MEIDE5/action/replication_record"}},"created_at":"2026-07-05T03:18:57.317064+00:00","updated_at":"2026-07-05T03:18:57.317064+00:00"}