{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:EVZSEM3RBHA3SL524P6XVZ5PY3","short_pith_number":"pith:EVZSEM3R","schema_version":"1.0","canonical_sha256":"257322337109c1b92fbae3fd7ae7afc6de0e6eea9e0732f01cbb905a5214320f","source":{"kind":"arxiv","id":"1903.11647","version":1},"attestation_state":"computed","paper":{"title":"Approximate Bayesian inference for multivariate point pattern analysis in disease mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Francisco Palmi-Perales, Gonzalo Lopez-Abente, Jose Miguel Sanz-Anquela, Pablo Fernandez-Navarro, Rebeca Ramis-Prieto, Virgilio Gomez-Rubio","submitted_at":"2019-03-27T18:52:38Z","abstract_excerpt":"We present a novel approach for the analysis of multivariate case-control georeferenced data using Bayesian inference in the context of disease mapping, where the spatial distribution of different types of cancers is analyzed. Extending other methodology in point pattern analysis, we propose a log-Gaussian Cox process for point pattern of cases and the controls, which accounts for risk factors, such as exposure to pollution sources, and includes a term to measure spatial residual variation.\n  For each disease, its intensity is modeled on a baseline spatial effect (estimated from both controls "},"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":"1903.11647","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-03-27T18:52:38Z","cross_cats_sorted":[],"title_canon_sha256":"bcda7c6fc2ddef7646d385232e7d3f163f823f217e712f45be4e1cc0d26c77dc","abstract_canon_sha256":"704b70460ef3692c12eac950eb7292bf6a1c1c0e2799101f8d6526b38e2238d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:02.133089Z","signature_b64":"CNxYB35Rr5ytu/lxQ4Ey6IkT7h3/ZGgpQHwZgvg9Cdjde1JDIutcrexzZQ58UD+rpHyz1xhxdq+8MseME79HBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"257322337109c1b92fbae3fd7ae7afc6de0e6eea9e0732f01cbb905a5214320f","last_reissued_at":"2026-05-17T23:50:02.132588Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:02.132588Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximate Bayesian inference for multivariate point pattern analysis in disease mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Francisco Palmi-Perales, Gonzalo Lopez-Abente, Jose Miguel Sanz-Anquela, Pablo Fernandez-Navarro, Rebeca Ramis-Prieto, Virgilio Gomez-Rubio","submitted_at":"2019-03-27T18:52:38Z","abstract_excerpt":"We present a novel approach for the analysis of multivariate case-control georeferenced data using Bayesian inference in the context of disease mapping, where the spatial distribution of different types of cancers is analyzed. Extending other methodology in point pattern analysis, we propose a log-Gaussian Cox process for point pattern of cases and the controls, which accounts for risk factors, such as exposure to pollution sources, and includes a term to measure spatial residual variation.\n  For each disease, its intensity is modeled on a baseline spatial effect (estimated from both controls "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11647","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":"1903.11647","created_at":"2026-05-17T23:50:02.132679+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.11647v1","created_at":"2026-05-17T23:50:02.132679+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11647","created_at":"2026-05-17T23:50:02.132679+00:00"},{"alias_kind":"pith_short_12","alias_value":"EVZSEM3RBHA3","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"EVZSEM3RBHA3SL52","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"EVZSEM3R","created_at":"2026-05-18T12:33:15.570797+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/EVZSEM3RBHA3SL524P6XVZ5PY3","json":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3.json","graph_json":"https://pith.science/api/pith-number/EVZSEM3RBHA3SL524P6XVZ5PY3/graph.json","events_json":"https://pith.science/api/pith-number/EVZSEM3RBHA3SL524P6XVZ5PY3/events.json","paper":"https://pith.science/paper/EVZSEM3R"},"agent_actions":{"view_html":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3","download_json":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3.json","view_paper":"https://pith.science/paper/EVZSEM3R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.11647&json=true","fetch_graph":"https://pith.science/api/pith-number/EVZSEM3RBHA3SL524P6XVZ5PY3/graph.json","fetch_events":"https://pith.science/api/pith-number/EVZSEM3RBHA3SL524P6XVZ5PY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3/action/storage_attestation","attest_author":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3/action/author_attestation","sign_citation":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3/action/citation_signature","submit_replication":"https://pith.science/pith/EVZSEM3RBHA3SL524P6XVZ5PY3/action/replication_record"}},"created_at":"2026-05-17T23:50:02.132679+00:00","updated_at":"2026-05-17T23:50:02.132679+00:00"}