{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:O5Z5MB6ECGV3QXFJYHQ47H2FGX","short_pith_number":"pith:O5Z5MB6E","schema_version":"1.0","canonical_sha256":"7773d607c411abb85ca9c1e1cf9f4535cbf29a671a7708a24d4e80ea7761bfae","source":{"kind":"arxiv","id":"1008.1550","version":1},"attestation_state":"computed","paper":{"title":"Hyper-g Priors for Generalized Linear Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Daniel Saban\\'es Bov\\'e, Leonhard Held","submitted_at":"2010-08-09T17:11:17Z","abstract_excerpt":"We develop an extension of the classical Zellner's g-prior to generalized linear models. The prior on the hyperparameter g is handled in a flexible way, so that any continuous proper hyperprior f(g) can be used, giving rise to a large class of hyper-g priors. Connections with the literature are described in detail. A fast and accurate integrated Laplace approximation of the marginal likelihood makes inference in large model spaces feasible. For posterior parameter estimation we propose an efficient and tuning-free Metropolis-Hastings sampler. The methodology is illustrated with variable select"},"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":"1008.1550","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-08-09T17:11:17Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"78b4a775670cf1a664759d5a584f86a9ddd4b95314a2eb0ddd79fe44ffb087d7","abstract_canon_sha256":"b00694d5c52d033859354d01fad06353c93e0e9fce65ca6b1e7b102bae4b11ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:14:16.633395Z","signature_b64":"yXHchJmZF/qg1BBhPV96XumjMY8pVsQVLrdyFNg0H9C9krEp8G/nkwvs/rsA6szYgZUgTr70Y3J4JALS34cqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7773d607c411abb85ca9c1e1cf9f4535cbf29a671a7708a24d4e80ea7761bfae","last_reissued_at":"2026-05-18T04:14:16.632804Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:14:16.632804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hyper-g Priors for Generalized Linear Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Daniel Saban\\'es Bov\\'e, Leonhard Held","submitted_at":"2010-08-09T17:11:17Z","abstract_excerpt":"We develop an extension of the classical Zellner's g-prior to generalized linear models. The prior on the hyperparameter g is handled in a flexible way, so that any continuous proper hyperprior f(g) can be used, giving rise to a large class of hyper-g priors. Connections with the literature are described in detail. A fast and accurate integrated Laplace approximation of the marginal likelihood makes inference in large model spaces feasible. For posterior parameter estimation we propose an efficient and tuning-free Metropolis-Hastings sampler. The methodology is illustrated with variable select"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1008.1550","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":"1008.1550","created_at":"2026-05-18T04:14:16.632899+00:00"},{"alias_kind":"arxiv_version","alias_value":"1008.1550v1","created_at":"2026-05-18T04:14:16.632899+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1008.1550","created_at":"2026-05-18T04:14:16.632899+00:00"},{"alias_kind":"pith_short_12","alias_value":"O5Z5MB6ECGV3","created_at":"2026-05-18T12:26:12.377268+00:00"},{"alias_kind":"pith_short_16","alias_value":"O5Z5MB6ECGV3QXFJ","created_at":"2026-05-18T12:26:12.377268+00:00"},{"alias_kind":"pith_short_8","alias_value":"O5Z5MB6E","created_at":"2026-05-18T12:26:12.377268+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/O5Z5MB6ECGV3QXFJYHQ47H2FGX","json":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX.json","graph_json":"https://pith.science/api/pith-number/O5Z5MB6ECGV3QXFJYHQ47H2FGX/graph.json","events_json":"https://pith.science/api/pith-number/O5Z5MB6ECGV3QXFJYHQ47H2FGX/events.json","paper":"https://pith.science/paper/O5Z5MB6E"},"agent_actions":{"view_html":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX","download_json":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX.json","view_paper":"https://pith.science/paper/O5Z5MB6E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1008.1550&json=true","fetch_graph":"https://pith.science/api/pith-number/O5Z5MB6ECGV3QXFJYHQ47H2FGX/graph.json","fetch_events":"https://pith.science/api/pith-number/O5Z5MB6ECGV3QXFJYHQ47H2FGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX/action/storage_attestation","attest_author":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX/action/author_attestation","sign_citation":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX/action/citation_signature","submit_replication":"https://pith.science/pith/O5Z5MB6ECGV3QXFJYHQ47H2FGX/action/replication_record"}},"created_at":"2026-05-18T04:14:16.632899+00:00","updated_at":"2026-05-18T04:14:16.632899+00:00"}