{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:GSJMVNQEW6BLXY7VRE37L2DOSN","short_pith_number":"pith:GSJMVNQE","schema_version":"1.0","canonical_sha256":"3492cab604b782bbe3f58937f5e86e9349149c8f83d0d33b78e41b6e86772a80","source":{"kind":"arxiv","id":"1411.6144","version":2},"attestation_state":"computed","paper":{"title":"False discovery rate smoothing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"James G. Scott, Oluwasanmi Koyejo, Russell A. Poldrack, Wesley Tansey","submitted_at":"2014-11-22T17:17:46Z","abstract_excerpt":"We present false discovery rate smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false-discovery rate at a given level. This results in increased power and cleaner spatial separation of signals from noise. The approach requires solving a non-standard high-dimensional optimization problem, for which an e"},"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":"1411.6144","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-11-22T17:17:46Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"2e9f193a6e19939677aac929e16dcb0fe28cfc703d56e55580981b645fb4c826","abstract_canon_sha256":"bad1e6b12fa089dc69fc0835a2e817a33912cf6f7b675134922fcdc63baeb7ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:59:27.916462Z","signature_b64":"9B4uJMOwQJn9YpSH8trk4sGfbBcH81a84kH7ZGDZNKtWTWMG+IvD/kIuBmIjMhNriYlITD/fUQ4rEJ8Te7mlBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3492cab604b782bbe3f58937f5e86e9349149c8f83d0d33b78e41b6e86772a80","last_reissued_at":"2026-05-18T00:59:27.915737Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:59:27.915737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"False discovery rate smoothing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"James G. Scott, Oluwasanmi Koyejo, Russell A. Poldrack, Wesley Tansey","submitted_at":"2014-11-22T17:17:46Z","abstract_excerpt":"We present false discovery rate smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false-discovery rate at a given level. This results in increased power and cleaner spatial separation of signals from noise. The approach requires solving a non-standard high-dimensional optimization problem, for which an e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.6144","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":"1411.6144","created_at":"2026-05-18T00:59:27.915851+00:00"},{"alias_kind":"arxiv_version","alias_value":"1411.6144v2","created_at":"2026-05-18T00:59:27.915851+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.6144","created_at":"2026-05-18T00:59:27.915851+00:00"},{"alias_kind":"pith_short_12","alias_value":"GSJMVNQEW6BL","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_16","alias_value":"GSJMVNQEW6BLXY7V","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_8","alias_value":"GSJMVNQE","created_at":"2026-05-18T12:28:30.664211+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.17559","citing_title":"Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN","json":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN.json","graph_json":"https://pith.science/api/pith-number/GSJMVNQEW6BLXY7VRE37L2DOSN/graph.json","events_json":"https://pith.science/api/pith-number/GSJMVNQEW6BLXY7VRE37L2DOSN/events.json","paper":"https://pith.science/paper/GSJMVNQE"},"agent_actions":{"view_html":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN","download_json":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN.json","view_paper":"https://pith.science/paper/GSJMVNQE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1411.6144&json=true","fetch_graph":"https://pith.science/api/pith-number/GSJMVNQEW6BLXY7VRE37L2DOSN/graph.json","fetch_events":"https://pith.science/api/pith-number/GSJMVNQEW6BLXY7VRE37L2DOSN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN/action/storage_attestation","attest_author":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN/action/author_attestation","sign_citation":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN/action/citation_signature","submit_replication":"https://pith.science/pith/GSJMVNQEW6BLXY7VRE37L2DOSN/action/replication_record"}},"created_at":"2026-05-18T00:59:27.915851+00:00","updated_at":"2026-05-18T00:59:27.915851+00:00"}