{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:VEBYRCNQDNRTMABC4V4V75CBF7","short_pith_number":"pith:VEBYRCNQ","schema_version":"1.0","canonical_sha256":"a9038889b01b63360022e5795ff4412ffed44464333f2b73821a0a55304a7d09","source":{"kind":"arxiv","id":"1207.4854","version":3},"attestation_state":"computed","paper":{"title":"Finite sample posterior concentration in high-dimensional regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Artin Armagan, David Dunson, Lawrence Carin, Nate Strawn, Rayan Saab","submitted_at":"2012-07-20T05:52:24Z","abstract_excerpt":"We study the behavior of the posterior distribution in high-dimensional Bayesian Gaussian linear regression models having $p\\gg n$, with $p$ the number of predictors and $n$ the sample size. Our focus is on obtaining quantitative finite sample bounds ensuring sufficient posterior probability assigned in neighborhoods of the true regression coefficient vector, $\\beta^0$, with high probability. We assume that $\\beta^0$ is approximately $S$-sparse and obtain universal bounds, which provide insight into the role of the prior in controlling concentration of the posterior. Based on these finite samp"},"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":"1207.4854","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2012-07-20T05:52:24Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"662ae986861e0d1f16eb01b9623077637572faaa98362654227e870649383ece","abstract_canon_sha256":"69ee370fcafb19c0357b6f0692fbcc69fa084962df79aea42aad2c63208c4f81"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:03:22.182563Z","signature_b64":"hygPDWxx/5eflAoqmJXIaeZJf3A6wPnk/hqGqGhgXgdACuRErBJKZuxiLGo5TDlZF2YmwQ7fL9UabQEHNsiSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9038889b01b63360022e5795ff4412ffed44464333f2b73821a0a55304a7d09","last_reissued_at":"2026-05-18T03:03:22.181938Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:03:22.181938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finite sample posterior concentration in high-dimensional regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Artin Armagan, David Dunson, Lawrence Carin, Nate Strawn, Rayan Saab","submitted_at":"2012-07-20T05:52:24Z","abstract_excerpt":"We study the behavior of the posterior distribution in high-dimensional Bayesian Gaussian linear regression models having $p\\gg n$, with $p$ the number of predictors and $n$ the sample size. Our focus is on obtaining quantitative finite sample bounds ensuring sufficient posterior probability assigned in neighborhoods of the true regression coefficient vector, $\\beta^0$, with high probability. We assume that $\\beta^0$ is approximately $S$-sparse and obtain universal bounds, which provide insight into the role of the prior in controlling concentration of the posterior. Based on these finite samp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.4854","kind":"arxiv","version":3},"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":"1207.4854","created_at":"2026-05-18T03:03:22.182041+00:00"},{"alias_kind":"arxiv_version","alias_value":"1207.4854v3","created_at":"2026-05-18T03:03:22.182041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.4854","created_at":"2026-05-18T03:03:22.182041+00:00"},{"alias_kind":"pith_short_12","alias_value":"VEBYRCNQDNRT","created_at":"2026-05-18T12:27:25.539911+00:00"},{"alias_kind":"pith_short_16","alias_value":"VEBYRCNQDNRTMABC","created_at":"2026-05-18T12:27:25.539911+00:00"},{"alias_kind":"pith_short_8","alias_value":"VEBYRCNQ","created_at":"2026-05-18T12:27:25.539911+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/VEBYRCNQDNRTMABC4V4V75CBF7","json":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7.json","graph_json":"https://pith.science/api/pith-number/VEBYRCNQDNRTMABC4V4V75CBF7/graph.json","events_json":"https://pith.science/api/pith-number/VEBYRCNQDNRTMABC4V4V75CBF7/events.json","paper":"https://pith.science/paper/VEBYRCNQ"},"agent_actions":{"view_html":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7","download_json":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7.json","view_paper":"https://pith.science/paper/VEBYRCNQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1207.4854&json=true","fetch_graph":"https://pith.science/api/pith-number/VEBYRCNQDNRTMABC4V4V75CBF7/graph.json","fetch_events":"https://pith.science/api/pith-number/VEBYRCNQDNRTMABC4V4V75CBF7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7/action/storage_attestation","attest_author":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7/action/author_attestation","sign_citation":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7/action/citation_signature","submit_replication":"https://pith.science/pith/VEBYRCNQDNRTMABC4V4V75CBF7/action/replication_record"}},"created_at":"2026-05-18T03:03:22.182041+00:00","updated_at":"2026-05-18T03:03:22.182041+00:00"}