{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XKRGUEB43YLIVAD75YX2GE7MM2","short_pith_number":"pith:XKRGUEB4","schema_version":"1.0","canonical_sha256":"baa26a103cde168a807fee2fa313ec6691a5752ec016a37f5e6e0885a1d1848a","source":{"kind":"arxiv","id":"2605.24854","version":1},"attestation_state":"computed","paper":{"title":"Deep Regression for Repeated Measurements under Covariate Shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Wangli Xu, Xiangyu Xing, Yingxuan Wang","submitted_at":"2026-05-24T04:17:14Z","abstract_excerpt":"This paper studies nonparametric regression with repeated measurements when the response in the target domain is unobservable or costly to collect. We adopt a transfer learning framework that leverages a source domain with observable responses under covariate shift. The target regression function is estimated by correcting the distribution shift via the density ratio. We consider both known and unknown density ratio scenarios, which reflect different data available for nonparametric regression estimation. In both cases, we further address two settings: the uniformly bounded density ratio and t"},"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":"2605.24854","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-24T04:17:14Z","cross_cats_sorted":[],"title_canon_sha256":"12c6140ac961cbbede0238196943ed08cd4bf558720f5579e994bec92d533b05","abstract_canon_sha256":"6a96911e0fe72d7fc4a992432806fd3379a7183fb12d21eeefb30b3455d9edb5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:04:01.840160Z","signature_b64":"2u9Lj+9fCGhrwMXfWQeJQcXBnGYm1vGOfUQTDTpy0xoF+ik4eCfA+pQ2X76b9oXplLHZC/FsUE5PIYu6Fx2NBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"baa26a103cde168a807fee2fa313ec6691a5752ec016a37f5e6e0885a1d1848a","last_reissued_at":"2026-05-26T01:04:01.839307Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:04:01.839307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Regression for Repeated Measurements under Covariate Shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Wangli Xu, Xiangyu Xing, Yingxuan Wang","submitted_at":"2026-05-24T04:17:14Z","abstract_excerpt":"This paper studies nonparametric regression with repeated measurements when the response in the target domain is unobservable or costly to collect. We adopt a transfer learning framework that leverages a source domain with observable responses under covariate shift. The target regression function is estimated by correcting the distribution shift via the density ratio. We consider both known and unknown density ratio scenarios, which reflect different data available for nonparametric regression estimation. In both cases, we further address two settings: the uniformly bounded density ratio and t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24854","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/2605.24854/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":"2605.24854","created_at":"2026-05-26T01:04:01.839449+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.24854v1","created_at":"2026-05-26T01:04:01.839449+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24854","created_at":"2026-05-26T01:04:01.839449+00:00"},{"alias_kind":"pith_short_12","alias_value":"XKRGUEB43YLI","created_at":"2026-05-26T01:04:01.839449+00:00"},{"alias_kind":"pith_short_16","alias_value":"XKRGUEB43YLIVAD7","created_at":"2026-05-26T01:04:01.839449+00:00"},{"alias_kind":"pith_short_8","alias_value":"XKRGUEB4","created_at":"2026-05-26T01:04:01.839449+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/XKRGUEB43YLIVAD75YX2GE7MM2","json":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2.json","graph_json":"https://pith.science/api/pith-number/XKRGUEB43YLIVAD75YX2GE7MM2/graph.json","events_json":"https://pith.science/api/pith-number/XKRGUEB43YLIVAD75YX2GE7MM2/events.json","paper":"https://pith.science/paper/XKRGUEB4"},"agent_actions":{"view_html":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2","download_json":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2.json","view_paper":"https://pith.science/paper/XKRGUEB4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.24854&json=true","fetch_graph":"https://pith.science/api/pith-number/XKRGUEB43YLIVAD75YX2GE7MM2/graph.json","fetch_events":"https://pith.science/api/pith-number/XKRGUEB43YLIVAD75YX2GE7MM2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2/action/storage_attestation","attest_author":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2/action/author_attestation","sign_citation":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2/action/citation_signature","submit_replication":"https://pith.science/pith/XKRGUEB43YLIVAD75YX2GE7MM2/action/replication_record"}},"created_at":"2026-05-26T01:04:01.839449+00:00","updated_at":"2026-05-26T01:04:01.839449+00:00"}