{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6GUZFV6636P2H6AOIHEAEGSWGF","short_pith_number":"pith:6GUZFV66","schema_version":"1.0","canonical_sha256":"f1a992d7dedf9fa3f80e41c8021a563155250520f5f74d07a9a47b7ae284fee9","source":{"kind":"arxiv","id":"2505.22748","version":1},"attestation_state":"computed","paper":{"title":"Nonparametric Estimation of Conditional Survival Function with Time-Varying Covariates Using DeepONet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Bingqing Hu, Bin Nan","submitted_at":"2025-05-28T18:05:49Z","abstract_excerpt":"Traditional survival models often rely on restrictive assumptions such as proportional hazards or instantaneous effects of time-varying covariates on the hazard function, which limit their applicability in real-world settings. We consider the nonparametric estimation of the conditional survival function, which leverages the flexibility of neural networks to capture the complex, potentially long-term non-instantaneous effects of time-varying covariates. In this work, we use Deep Operator Networks (DeepONet), a deep learning architecture designed for operator learning, to model the arbitrary eff"},"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":"2505.22748","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-05-28T18:05:49Z","cross_cats_sorted":[],"title_canon_sha256":"df0707cc1fcbbfd7e09736e20dc086e873f7d08899aa2d6cfe156c15f5e771e9","abstract_canon_sha256":"c84dec4c75b9d806f827f528a6761300f584e3581a74972c9ed0329179d6d5d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:11:49.515801Z","signature_b64":"qHgTfGLDoHb8LKQf/PD3SHpr9ORAfF2X79UEz7OJi1+jYrEViDpHLftfWy7pPiVOWUCyrugSWinTgLkM5oGRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1a992d7dedf9fa3f80e41c8021a563155250520f5f74d07a9a47b7ae284fee9","last_reissued_at":"2026-07-05T11:11:49.515288Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:11:49.515288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Estimation of Conditional Survival Function with Time-Varying Covariates Using DeepONet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Bingqing Hu, Bin Nan","submitted_at":"2025-05-28T18:05:49Z","abstract_excerpt":"Traditional survival models often rely on restrictive assumptions such as proportional hazards or instantaneous effects of time-varying covariates on the hazard function, which limit their applicability in real-world settings. We consider the nonparametric estimation of the conditional survival function, which leverages the flexibility of neural networks to capture the complex, potentially long-term non-instantaneous effects of time-varying covariates. In this work, we use Deep Operator Networks (DeepONet), a deep learning architecture designed for operator learning, to model the arbitrary eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.22748","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/2505.22748/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":"2505.22748","created_at":"2026-07-05T11:11:49.515357+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.22748v1","created_at":"2026-07-05T11:11:49.515357+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.22748","created_at":"2026-07-05T11:11:49.515357+00:00"},{"alias_kind":"pith_short_12","alias_value":"6GUZFV6636P2","created_at":"2026-07-05T11:11:49.515357+00:00"},{"alias_kind":"pith_short_16","alias_value":"6GUZFV6636P2H6AO","created_at":"2026-07-05T11:11:49.515357+00:00"},{"alias_kind":"pith_short_8","alias_value":"6GUZFV66","created_at":"2026-07-05T11:11:49.515357+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/6GUZFV6636P2H6AOIHEAEGSWGF","json":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF.json","graph_json":"https://pith.science/api/pith-number/6GUZFV6636P2H6AOIHEAEGSWGF/graph.json","events_json":"https://pith.science/api/pith-number/6GUZFV6636P2H6AOIHEAEGSWGF/events.json","paper":"https://pith.science/paper/6GUZFV66"},"agent_actions":{"view_html":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF","download_json":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF.json","view_paper":"https://pith.science/paper/6GUZFV66","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.22748&json=true","fetch_graph":"https://pith.science/api/pith-number/6GUZFV6636P2H6AOIHEAEGSWGF/graph.json","fetch_events":"https://pith.science/api/pith-number/6GUZFV6636P2H6AOIHEAEGSWGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF/action/storage_attestation","attest_author":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF/action/author_attestation","sign_citation":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF/action/citation_signature","submit_replication":"https://pith.science/pith/6GUZFV6636P2H6AOIHEAEGSWGF/action/replication_record"}},"created_at":"2026-07-05T11:11:49.515357+00:00","updated_at":"2026-07-05T11:11:49.515357+00:00"}