{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:WU3DIGINI3HCYT34D46OGK3ARZ","short_pith_number":"pith:WU3DIGIN","schema_version":"1.0","canonical_sha256":"b53634190d46ce2c4f7c1f3ce32b608e7c598159cb563e50188baefeed954364","source":{"kind":"arxiv","id":"1108.3520","version":2},"attestation_state":"computed","paper":{"title":"Mixtures of g-Priors for Generalised Additive Model Selection with Penalised Splines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Daniel Saban\\'es Bov\\'e, G\\\"oran Kauermann, Leonhard Held","submitted_at":"2011-08-17T16:53:46Z","abstract_excerpt":"We propose an objective Bayesian approach to the selection of covariates and their penalised splines transformations in generalised additive models. Specification of a reasonable default prior for the model parameters and combination with a multiplicity-correction prior for the models themselves is crucial for this task. Here we use well-studied and well-behaved continuous mixtures of g-priors as default priors. We introduce the methodology in the normal model and extend it to non-normal exponential families. A simulation study and an application from the literature illustrate the proposed app"},"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":"1108.3520","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-08-17T16:53:46Z","cross_cats_sorted":[],"title_canon_sha256":"8bcc2fb7d9afb21f595d25c004c1ac390f5f4b7e483ca0cd63ab489491edf6d1","abstract_canon_sha256":"27e31c15aa8215fb79b7be6453ee1c864b374aa2cbfed1ab4c12965e77e2e960"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:48:34.173783Z","signature_b64":"D3LZbtMOR15R68V9TZcV+yKxTJLL7yRmTcDdG7g2CqtsVS9VFXwuw7S0GvRSnNClpgl3pCtACFNVWYwsSEXfAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b53634190d46ce2c4f7c1f3ce32b608e7c598159cb563e50188baefeed954364","last_reissued_at":"2026-05-18T03:48:34.173263Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:48:34.173263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mixtures of g-Priors for Generalised Additive Model Selection with Penalised Splines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Daniel Saban\\'es Bov\\'e, G\\\"oran Kauermann, Leonhard Held","submitted_at":"2011-08-17T16:53:46Z","abstract_excerpt":"We propose an objective Bayesian approach to the selection of covariates and their penalised splines transformations in generalised additive models. Specification of a reasonable default prior for the model parameters and combination with a multiplicity-correction prior for the models themselves is crucial for this task. Here we use well-studied and well-behaved continuous mixtures of g-priors as default priors. We introduce the methodology in the normal model and extend it to non-normal exponential families. A simulation study and an application from the literature illustrate the proposed app"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1108.3520","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":"1108.3520","created_at":"2026-05-18T03:48:34.173351+00:00"},{"alias_kind":"arxiv_version","alias_value":"1108.3520v2","created_at":"2026-05-18T03:48:34.173351+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1108.3520","created_at":"2026-05-18T03:48:34.173351+00:00"},{"alias_kind":"pith_short_12","alias_value":"WU3DIGINI3HC","created_at":"2026-05-18T12:26:44.992195+00:00"},{"alias_kind":"pith_short_16","alias_value":"WU3DIGINI3HCYT34","created_at":"2026-05-18T12:26:44.992195+00:00"},{"alias_kind":"pith_short_8","alias_value":"WU3DIGIN","created_at":"2026-05-18T12:26:44.992195+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/WU3DIGINI3HCYT34D46OGK3ARZ","json":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ.json","graph_json":"https://pith.science/api/pith-number/WU3DIGINI3HCYT34D46OGK3ARZ/graph.json","events_json":"https://pith.science/api/pith-number/WU3DIGINI3HCYT34D46OGK3ARZ/events.json","paper":"https://pith.science/paper/WU3DIGIN"},"agent_actions":{"view_html":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ","download_json":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ.json","view_paper":"https://pith.science/paper/WU3DIGIN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1108.3520&json=true","fetch_graph":"https://pith.science/api/pith-number/WU3DIGINI3HCYT34D46OGK3ARZ/graph.json","fetch_events":"https://pith.science/api/pith-number/WU3DIGINI3HCYT34D46OGK3ARZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ/action/storage_attestation","attest_author":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ/action/author_attestation","sign_citation":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ/action/citation_signature","submit_replication":"https://pith.science/pith/WU3DIGINI3HCYT34D46OGK3ARZ/action/replication_record"}},"created_at":"2026-05-18T03:48:34.173351+00:00","updated_at":"2026-05-18T03:48:34.173351+00:00"}