{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2K5XTIK2UVZN6O3F6YVMHLIOJ6","short_pith_number":"pith:2K5XTIK2","schema_version":"1.0","canonical_sha256":"d2bb79a15aa572df3b65f62ac3ad0e4fb082a0fd34ccebc28415a3c428ff3be2","source":{"kind":"arxiv","id":"2507.09559","version":1},"attestation_state":"computed","paper":{"title":"The Use of Variational Inference for Lifetime Data with Spatial Correlations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Laura Freeman, Xinwei Deng, Yili Hong, Yueyao Wang","submitted_at":"2025-07-13T10:08:44Z","abstract_excerpt":"Lifetime data with spatial correlations are often collected for analysis in modern engineering, clinical, and medical applications. For such spatial lifetime data, statistical models usually account for the spatial dependence through spatial random effects, such as the cumulative exposure model and the proportional hazards model. For these models, the Bayesian estimation is commonly used for model inference, but often encounters computational challenges when the number of spatial locations is large. The conventional Markov Chain Monte Carlo (MCMC) methods for sampling the posterior can be time"},"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":"2507.09559","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-07-13T10:08:44Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"fd4bc33f27d8912fe874290aef1a7bd7ce0a177b8bd44e4a31a21d1dde7d73d3","abstract_canon_sha256":"f1aa9f669bc51b293e57a14e43f6211abff70644fe1babb0fc79206a23e6fb98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:11.279358Z","signature_b64":"xiktG1ak0IcY2+F51tJCXEosGFUOlQ1/1Rej4e+XXSKiXzEx5vGQ/svUkYscAKCMneLTEbZo2eroPFyK+mY8AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2bb79a15aa572df3b65f62ac3ad0e4fb082a0fd34ccebc28415a3c428ff3be2","last_reissued_at":"2026-07-05T11:36:11.278790Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:11.278790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Use of Variational Inference for Lifetime Data with Spatial Correlations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Laura Freeman, Xinwei Deng, Yili Hong, Yueyao Wang","submitted_at":"2025-07-13T10:08:44Z","abstract_excerpt":"Lifetime data with spatial correlations are often collected for analysis in modern engineering, clinical, and medical applications. For such spatial lifetime data, statistical models usually account for the spatial dependence through spatial random effects, such as the cumulative exposure model and the proportional hazards model. For these models, the Bayesian estimation is commonly used for model inference, but often encounters computational challenges when the number of spatial locations is large. The conventional Markov Chain Monte Carlo (MCMC) methods for sampling the posterior can be time"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09559","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.09559/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2507.09559","created_at":"2026-07-05T11:36:11.278870+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.09559v1","created_at":"2026-07-05T11:36:11.278870+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09559","created_at":"2026-07-05T11:36:11.278870+00:00"},{"alias_kind":"pith_short_12","alias_value":"2K5XTIK2UVZN","created_at":"2026-07-05T11:36:11.278870+00:00"},{"alias_kind":"pith_short_16","alias_value":"2K5XTIK2UVZN6O3F","created_at":"2026-07-05T11:36:11.278870+00:00"},{"alias_kind":"pith_short_8","alias_value":"2K5XTIK2","created_at":"2026-07-05T11:36:11.278870+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20451","citing_title":"SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6","json":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6.json","graph_json":"https://pith.science/api/pith-number/2K5XTIK2UVZN6O3F6YVMHLIOJ6/graph.json","events_json":"https://pith.science/api/pith-number/2K5XTIK2UVZN6O3F6YVMHLIOJ6/events.json","paper":"https://pith.science/paper/2K5XTIK2"},"agent_actions":{"view_html":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6","download_json":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6.json","view_paper":"https://pith.science/paper/2K5XTIK2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.09559&json=true","fetch_graph":"https://pith.science/api/pith-number/2K5XTIK2UVZN6O3F6YVMHLIOJ6/graph.json","fetch_events":"https://pith.science/api/pith-number/2K5XTIK2UVZN6O3F6YVMHLIOJ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6/action/storage_attestation","attest_author":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6/action/author_attestation","sign_citation":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6/action/citation_signature","submit_replication":"https://pith.science/pith/2K5XTIK2UVZN6O3F6YVMHLIOJ6/action/replication_record"}},"created_at":"2026-07-05T11:36:11.278870+00:00","updated_at":"2026-07-05T11:36:11.278870+00:00"}