{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:J7Q5RZTSAGXVX4AHCPIGKS7QOD","short_pith_number":"pith:J7Q5RZTS","schema_version":"1.0","canonical_sha256":"4fe1d8e67201af5bf00713d0654bf070e881c07e66d6dbcad323ff5d0b2f9290","source":{"kind":"arxiv","id":"1206.1024","version":2},"attestation_state":"computed","paper":{"title":"Conditional Sure Independence Screening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Anneleen Verhasselt, Emre Barut, Jianqing Fan","submitted_at":"2012-06-05T19:06:58Z","abstract_excerpt":"Independence screening is a powerful method for variable selection for `Big Data' when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or variations of it. In many applications, researchers often have some prior knowledge that a certain set of variables is related to the response. In such a situation, a natural assessment on the relative importance of the other predictors is the conditional contributions of the individual predictors in presence of the known set of variables. This results in conditional sure independence screen"},"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":"1206.1024","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2012-06-05T19:06:58Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"0be313809a8905de014bdc3d9b6dd141d1d818edb07ed11b0c06f1383dcaec38","abstract_canon_sha256":"4c2f3c665f732fa6de544f8697082f8f0bc19315aecc5290fe329eac669cf59f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:41:43.877785Z","signature_b64":"EQEP2c34WqfRofIg9Yr6X1NktlE4ihXElORmuTU2DeUDIq5jT1P3fjIGexXhiSaYZHnDjKDeRN+h4Ft0937LBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fe1d8e67201af5bf00713d0654bf070e881c07e66d6dbcad323ff5d0b2f9290","last_reissued_at":"2026-05-18T03:41:43.876827Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:41:43.876827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional Sure Independence Screening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Anneleen Verhasselt, Emre Barut, Jianqing Fan","submitted_at":"2012-06-05T19:06:58Z","abstract_excerpt":"Independence screening is a powerful method for variable selection for `Big Data' when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or variations of it. In many applications, researchers often have some prior knowledge that a certain set of variables is related to the response. In such a situation, a natural assessment on the relative importance of the other predictors is the conditional contributions of the individual predictors in presence of the known set of variables. This results in conditional sure independence screen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.1024","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":""},"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":"1206.1024","created_at":"2026-05-18T03:41:43.876988+00:00"},{"alias_kind":"arxiv_version","alias_value":"1206.1024v2","created_at":"2026-05-18T03:41:43.876988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.1024","created_at":"2026-05-18T03:41:43.876988+00:00"},{"alias_kind":"pith_short_12","alias_value":"J7Q5RZTSAGXV","created_at":"2026-05-18T12:27:11.947152+00:00"},{"alias_kind":"pith_short_16","alias_value":"J7Q5RZTSAGXVX4AH","created_at":"2026-05-18T12:27:11.947152+00:00"},{"alias_kind":"pith_short_8","alias_value":"J7Q5RZTS","created_at":"2026-05-18T12:27:11.947152+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/J7Q5RZTSAGXVX4AHCPIGKS7QOD","json":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD.json","graph_json":"https://pith.science/api/pith-number/J7Q5RZTSAGXVX4AHCPIGKS7QOD/graph.json","events_json":"https://pith.science/api/pith-number/J7Q5RZTSAGXVX4AHCPIGKS7QOD/events.json","paper":"https://pith.science/paper/J7Q5RZTS"},"agent_actions":{"view_html":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD","download_json":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD.json","view_paper":"https://pith.science/paper/J7Q5RZTS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1206.1024&json=true","fetch_graph":"https://pith.science/api/pith-number/J7Q5RZTSAGXVX4AHCPIGKS7QOD/graph.json","fetch_events":"https://pith.science/api/pith-number/J7Q5RZTSAGXVX4AHCPIGKS7QOD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD/action/storage_attestation","attest_author":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD/action/author_attestation","sign_citation":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD/action/citation_signature","submit_replication":"https://pith.science/pith/J7Q5RZTSAGXVX4AHCPIGKS7QOD/action/replication_record"}},"created_at":"2026-05-18T03:41:43.876988+00:00","updated_at":"2026-05-18T03:41:43.876988+00:00"}