{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EU443BK5DWLR5BP6WHHD3WIYP6","short_pith_number":"pith:EU443BK5","schema_version":"1.0","canonical_sha256":"2539cd855d1d971e85feb1ce3dd9187f8555f135b0fa4bb4e5d6539a1c192535","source":{"kind":"arxiv","id":"2501.01610","version":1},"attestation_state":"computed","paper":{"title":"Bootstrap Nonparametric Inference under Data Integration","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chong Jin, Peijun Sang, Zuofeng Shang","submitted_at":"2025-01-03T03:09:33Z","abstract_excerpt":"We propose multiplier bootstrap procedures for nonparametric inference and uncertainty quantification of the target mean function, based on a novel framework of integrating target and source data. We begin with the relatively easier covariate shift scenario with equal target and source mean functions and propose estimation and inferential procedures through a straightforward combination of all target and source datasets. We next consider the more general and flexible distribution shift scenario with arbitrary target and source mean functions, and propose a two-step inferential procedure. First"},"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":"2501.01610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2025-01-03T03:09:33Z","cross_cats_sorted":[],"title_canon_sha256":"4502e34799dfff02af97d61b4af7086a7a1e8057cbec00403134427a105ece07","abstract_canon_sha256":"3aaacef946496f2f9cc41c19225ddd50eefe5a5827fa55b537acd993771a474f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:27.486875Z","signature_b64":"Cm+bzPyYx//VtUkUF2/qJDTD+am0ijn6sxyw+vbY04H6B7snauUXcnvFjZ28mD9G/2/S1nkZB8KXonTmQjRNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2539cd855d1d971e85feb1ce3dd9187f8555f135b0fa4bb4e5d6539a1c192535","last_reissued_at":"2026-07-05T09:56:27.486422Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:27.486422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bootstrap Nonparametric Inference under Data Integration","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chong Jin, Peijun Sang, Zuofeng Shang","submitted_at":"2025-01-03T03:09:33Z","abstract_excerpt":"We propose multiplier bootstrap procedures for nonparametric inference and uncertainty quantification of the target mean function, based on a novel framework of integrating target and source data. We begin with the relatively easier covariate shift scenario with equal target and source mean functions and propose estimation and inferential procedures through a straightforward combination of all target and source datasets. We next consider the more general and flexible distribution shift scenario with arbitrary target and source mean functions, and propose a two-step inferential procedure. First"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01610","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/2501.01610/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":"2501.01610","created_at":"2026-07-05T09:56:27.486480+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01610v1","created_at":"2026-07-05T09:56:27.486480+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01610","created_at":"2026-07-05T09:56:27.486480+00:00"},{"alias_kind":"pith_short_12","alias_value":"EU443BK5DWLR","created_at":"2026-07-05T09:56:27.486480+00:00"},{"alias_kind":"pith_short_16","alias_value":"EU443BK5DWLR5BP6","created_at":"2026-07-05T09:56:27.486480+00:00"},{"alias_kind":"pith_short_8","alias_value":"EU443BK5","created_at":"2026-07-05T09:56:27.486480+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04860","citing_title":"Nonparametric Goodness-of-fit Testing under Covariate Shift","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6","json":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6.json","graph_json":"https://pith.science/api/pith-number/EU443BK5DWLR5BP6WHHD3WIYP6/graph.json","events_json":"https://pith.science/api/pith-number/EU443BK5DWLR5BP6WHHD3WIYP6/events.json","paper":"https://pith.science/paper/EU443BK5"},"agent_actions":{"view_html":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6","download_json":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6.json","view_paper":"https://pith.science/paper/EU443BK5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01610&json=true","fetch_graph":"https://pith.science/api/pith-number/EU443BK5DWLR5BP6WHHD3WIYP6/graph.json","fetch_events":"https://pith.science/api/pith-number/EU443BK5DWLR5BP6WHHD3WIYP6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6/action/storage_attestation","attest_author":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6/action/author_attestation","sign_citation":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6/action/citation_signature","submit_replication":"https://pith.science/pith/EU443BK5DWLR5BP6WHHD3WIYP6/action/replication_record"}},"created_at":"2026-07-05T09:56:27.486480+00:00","updated_at":"2026-07-05T09:56:27.486480+00:00"}