{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:M6AEVZGIQOOYGKKOIFEAQN7BHN","short_pith_number":"pith:M6AEVZGI","schema_version":"1.0","canonical_sha256":"67804ae4c8839d83294e41480837e13b5bbe3917937f3d95de46f9b37256ca96","source":{"kind":"arxiv","id":"1703.09163","version":2},"attestation_state":"computed","paper":{"title":"Scalable Bayesian shrinkage and uncertainty quantification in high-dimensional regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Bala Rajaratnam, Doug Sparks, Kshitij Khare, Liyuan Zhang","submitted_at":"2017-03-27T16:14:18Z","abstract_excerpt":"Bayesian shrinkage methods have generated a lot of recent interest as tools for high-dimensional regression and model selection. These methods naturally facilitate tractable uncertainty quantification and incorporation of prior information. A common feature of these models, including the Bayesian lasso, global-local shrinkage priors, and spike-and-slab priors is that the corresponding priors on the regression coefficients can be expressed as scale mixture of normals. While the three-step Gibbs sampler used to sample from the often intractable associated posterior density has been shown to be g"},"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":"1703.09163","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-03-27T16:14:18Z","cross_cats_sorted":[],"title_canon_sha256":"119b32d668d4f2cd356f37ed7070b4d9856690ace0fb2ce7fbe7007719ceb726","abstract_canon_sha256":"c91293a7fab8e10b725c873991f1ea63c5e3f4a0aaa3eb549a331eb8b98d5d65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:22.170395Z","signature_b64":"wVBneQE7vYK0IHdcZNef/Tw1Q2wedNqwBMl4p5QJgfTYSX4cGmUvaVtP8pabk6Un4D90ZZLPS1M/nl5zFJaRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67804ae4c8839d83294e41480837e13b5bbe3917937f3d95de46f9b37256ca96","last_reissued_at":"2026-05-18T00:46:22.169860Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:22.169860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Bayesian shrinkage and uncertainty quantification in high-dimensional regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Bala Rajaratnam, Doug Sparks, Kshitij Khare, Liyuan Zhang","submitted_at":"2017-03-27T16:14:18Z","abstract_excerpt":"Bayesian shrinkage methods have generated a lot of recent interest as tools for high-dimensional regression and model selection. These methods naturally facilitate tractable uncertainty quantification and incorporation of prior information. A common feature of these models, including the Bayesian lasso, global-local shrinkage priors, and spike-and-slab priors is that the corresponding priors on the regression coefficients can be expressed as scale mixture of normals. While the three-step Gibbs sampler used to sample from the often intractable associated posterior density has been shown to be g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09163","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":"1703.09163","created_at":"2026-05-18T00:46:22.169931+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.09163v2","created_at":"2026-05-18T00:46:22.169931+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09163","created_at":"2026-05-18T00:46:22.169931+00:00"},{"alias_kind":"pith_short_12","alias_value":"M6AEVZGIQOOY","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"M6AEVZGIQOOYGKKO","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"M6AEVZGI","created_at":"2026-05-18T12:31:28.150371+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/M6AEVZGIQOOYGKKOIFEAQN7BHN","json":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN.json","graph_json":"https://pith.science/api/pith-number/M6AEVZGIQOOYGKKOIFEAQN7BHN/graph.json","events_json":"https://pith.science/api/pith-number/M6AEVZGIQOOYGKKOIFEAQN7BHN/events.json","paper":"https://pith.science/paper/M6AEVZGI"},"agent_actions":{"view_html":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN","download_json":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN.json","view_paper":"https://pith.science/paper/M6AEVZGI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.09163&json=true","fetch_graph":"https://pith.science/api/pith-number/M6AEVZGIQOOYGKKOIFEAQN7BHN/graph.json","fetch_events":"https://pith.science/api/pith-number/M6AEVZGIQOOYGKKOIFEAQN7BHN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN/action/storage_attestation","attest_author":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN/action/author_attestation","sign_citation":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN/action/citation_signature","submit_replication":"https://pith.science/pith/M6AEVZGIQOOYGKKOIFEAQN7BHN/action/replication_record"}},"created_at":"2026-05-18T00:46:22.169931+00:00","updated_at":"2026-05-18T00:46:22.169931+00:00"}