{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UVAGVHYVRPVNJ3DXEEHSCOJ4YO","short_pith_number":"pith:UVAGVHYV","schema_version":"1.0","canonical_sha256":"a5406a9f158bead4ec77210f21393cc3b6e31b017312c5ff8fabfb501acb57fa","source":{"kind":"arxiv","id":"1706.03400","version":2},"attestation_state":"computed","paper":{"title":"A Prototype Knockoff Filter for Group Selection with FDR Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Anthony Hou, Jiajie Chen, Thomas Y. Hou","submitted_at":"2017-06-11T19:56:06Z","abstract_excerpt":"In many applications, we need to study a linear regression model that consists of a response variable and a large number of potential explanatory variables and determine which variables are truly associated with the response. In 2015, Barber and Candes introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. In this paper, we propose a prototype knockoff filter for group selection by extending the Reid-Tibshirani prototype method. Our prototype knockoff filter improves the computat"},"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":"1706.03400","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-06-11T19:56:06Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"93273247e1c128e0c935c07118169a1a29b5f8aa1e67cf9ae487eccdca29a4c2","abstract_canon_sha256":"93f2322520f37022a357de087af2e4e4d8724eab653ea59d5f2d93c780dd3a27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:04.817932Z","signature_b64":"1J3glNn86FhAWKAcMXiKsoJzhSs3EzsOvPKlABm9avYqHpANq6okNjo0Kt5T6Lyf146K4i4aYEqLQt7xRRdzAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5406a9f158bead4ec77210f21393cc3b6e31b017312c5ff8fabfb501acb57fa","last_reissued_at":"2026-05-17T23:40:04.817364Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:04.817364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Prototype Knockoff Filter for Group Selection with FDR Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Anthony Hou, Jiajie Chen, Thomas Y. Hou","submitted_at":"2017-06-11T19:56:06Z","abstract_excerpt":"In many applications, we need to study a linear regression model that consists of a response variable and a large number of potential explanatory variables and determine which variables are truly associated with the response. In 2015, Barber and Candes introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. In this paper, we propose a prototype knockoff filter for group selection by extending the Reid-Tibshirani prototype method. Our prototype knockoff filter improves the computat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03400","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":"1706.03400","created_at":"2026-05-17T23:40:04.817473+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.03400v2","created_at":"2026-05-17T23:40:04.817473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03400","created_at":"2026-05-17T23:40:04.817473+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVAGVHYVRPVN","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVAGVHYVRPVNJ3DX","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVAGVHYV","created_at":"2026-05-18T12:31:49.984773+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/UVAGVHYVRPVNJ3DXEEHSCOJ4YO","json":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO.json","graph_json":"https://pith.science/api/pith-number/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/graph.json","events_json":"https://pith.science/api/pith-number/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/events.json","paper":"https://pith.science/paper/UVAGVHYV"},"agent_actions":{"view_html":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO","download_json":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO.json","view_paper":"https://pith.science/paper/UVAGVHYV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.03400&json=true","fetch_graph":"https://pith.science/api/pith-number/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/graph.json","fetch_events":"https://pith.science/api/pith-number/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/action/storage_attestation","attest_author":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/action/author_attestation","sign_citation":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/action/citation_signature","submit_replication":"https://pith.science/pith/UVAGVHYVRPVNJ3DXEEHSCOJ4YO/action/replication_record"}},"created_at":"2026-05-17T23:40:04.817473+00:00","updated_at":"2026-05-17T23:40:04.817473+00:00"}