{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GOEVKSBPGG6EPTLUREMU7J46YJ","short_pith_number":"pith:GOEVKSBP","schema_version":"1.0","canonical_sha256":"338955482f31bc47cd7489194fa79ec25a8ff6c67ebd290e80e821c32887e730","source":{"kind":"arxiv","id":"1803.10439","version":1},"attestation_state":"computed","paper":{"title":"BIVAS: A scalable Bayesian method for bi-level variable selection with applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Can Yang, Heng Peng, Jingsi Ming, Jin Liu, Mingwei Dai, Mingxuan Cai","submitted_at":"2018-03-28T07:44:42Z","abstract_excerpt":"In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to each predictor. Examples include that genetic variants can be grouped at the gene level and a covariate from different tasks naturally forms a group. Thus, it is of interest to select important groups as well as important members from those groups. The existing Markov Chain Monte Carlo (MCMC) methods are often computationally intensive and not scalable to large data sets. To address this problem, we consider variati"},"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":"1803.10439","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-03-28T07:44:42Z","cross_cats_sorted":[],"title_canon_sha256":"2b8c279e32b44423f6fbf226c75de6757bd016aca64a8ea09b0a99a45257db07","abstract_canon_sha256":"de2cdc1ac2c77b2fa85e0262430ff5a29fc347deeed594381582d11ffeac9a25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:55.422363Z","signature_b64":"j4Lcg4k6OSCagtolEtDBxEm5FgSGNwK/yxXhJeycJu9BVClfr1lVuHCfx+xKVzE6eFTBqoomjajvEI1gg4g7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"338955482f31bc47cd7489194fa79ec25a8ff6c67ebd290e80e821c32887e730","last_reissued_at":"2026-05-18T00:19:55.421748Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:55.421748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BIVAS: A scalable Bayesian method for bi-level variable selection with applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Can Yang, Heng Peng, Jingsi Ming, Jin Liu, Mingwei Dai, Mingxuan Cai","submitted_at":"2018-03-28T07:44:42Z","abstract_excerpt":"In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to each predictor. Examples include that genetic variants can be grouped at the gene level and a covariate from different tasks naturally forms a group. Thus, it is of interest to select important groups as well as important members from those groups. The existing Markov Chain Monte Carlo (MCMC) methods are often computationally intensive and not scalable to large data sets. To address this problem, we consider variati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10439","kind":"arxiv","version":1},"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":"1803.10439","created_at":"2026-05-18T00:19:55.421849+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.10439v1","created_at":"2026-05-18T00:19:55.421849+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10439","created_at":"2026-05-18T00:19:55.421849+00:00"},{"alias_kind":"pith_short_12","alias_value":"GOEVKSBPGG6E","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GOEVKSBPGG6EPTLU","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GOEVKSBP","created_at":"2026-05-18T12:32:25.280505+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/GOEVKSBPGG6EPTLUREMU7J46YJ","json":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ.json","graph_json":"https://pith.science/api/pith-number/GOEVKSBPGG6EPTLUREMU7J46YJ/graph.json","events_json":"https://pith.science/api/pith-number/GOEVKSBPGG6EPTLUREMU7J46YJ/events.json","paper":"https://pith.science/paper/GOEVKSBP"},"agent_actions":{"view_html":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ","download_json":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ.json","view_paper":"https://pith.science/paper/GOEVKSBP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.10439&json=true","fetch_graph":"https://pith.science/api/pith-number/GOEVKSBPGG6EPTLUREMU7J46YJ/graph.json","fetch_events":"https://pith.science/api/pith-number/GOEVKSBPGG6EPTLUREMU7J46YJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ/action/storage_attestation","attest_author":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ/action/author_attestation","sign_citation":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ/action/citation_signature","submit_replication":"https://pith.science/pith/GOEVKSBPGG6EPTLUREMU7J46YJ/action/replication_record"}},"created_at":"2026-05-18T00:19:55.421849+00:00","updated_at":"2026-05-18T00:19:55.421849+00:00"}