{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WEZWZR6ES5M5ZJJ6PL7KUGVFLJ","short_pith_number":"pith:WEZWZR6E","schema_version":"1.0","canonical_sha256":"b1336cc7c49759dca53e7afeaa1aa55a73f2ac77abed42f6fe4acec4a90f4ad4","source":{"kind":"arxiv","id":"2607.22493","version":1},"attestation_state":"computed","paper":{"title":"The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.ST","stat.CO","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Ashkan Ertefaie, Mark van der Laan, Yi Li","submitted_at":"2026-07-24T17:05:54Z","abstract_excerpt":"For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements. Although its validity is well understood for smooth parametric estimators, its theoretical properties for many modern semiparametric and machine-learning estimators remain largely unstudied. Nevertheless, bootstrap procedures are often used routinely in such settings, even when their validity is unknown and their computational cost is substantial. We develop the $V$-fold jackknife as a computationally efficient and theoretically justified alternativ"},"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":"2607.22493","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-07-24T17:05:54Z","cross_cats_sorted":["math.ST","stat.CO","stat.ML","stat.TH"],"title_canon_sha256":"40094b309c0125f15d64fbaab20e5b0af95c5fab97433b2aa4840e64c0ea95b8","abstract_canon_sha256":"6244b5bc804891faa3d64d9cfdaa291749b3f7564355104bcdb77e70eea2fa6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T01:21:23.821394Z","signature_b64":"+r7JE5DJ6KBjKk7frcDjWQjtuYTJ2xarD25kaW8yp6QfGE77mkjpY9zCwOa+SGYlroH9jsGkfm6KvqPRpxD7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1336cc7c49759dca53e7afeaa1aa55a73f2ac77abed42f6fe4acec4a90f4ad4","last_reissued_at":"2026-07-27T01:21:23.820527Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T01:21:23.820527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.ST","stat.CO","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Ashkan Ertefaie, Mark van der Laan, Yi Li","submitted_at":"2026-07-24T17:05:54Z","abstract_excerpt":"For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements. Although its validity is well understood for smooth parametric estimators, its theoretical properties for many modern semiparametric and machine-learning estimators remain largely unstudied. Nevertheless, bootstrap procedures are often used routinely in such settings, even when their validity is unknown and their computational cost is substantial. We develop the $V$-fold jackknife as a computationally efficient and theoretically justified alternativ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22493","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/2607.22493/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":"2607.22493","created_at":"2026-07-27T01:21:23.820978+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22493v1","created_at":"2026-07-27T01:21:23.820978+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22493","created_at":"2026-07-27T01:21:23.820978+00:00"},{"alias_kind":"pith_short_12","alias_value":"WEZWZR6ES5M5","created_at":"2026-07-27T01:21:23.820978+00:00"},{"alias_kind":"pith_short_16","alias_value":"WEZWZR6ES5M5ZJJ6","created_at":"2026-07-27T01:21:23.820978+00:00"},{"alias_kind":"pith_short_8","alias_value":"WEZWZR6E","created_at":"2026-07-27T01:21:23.820978+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/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ","json":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ.json","graph_json":"https://pith.science/api/pith-number/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/graph.json","events_json":"https://pith.science/api/pith-number/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/events.json","paper":"https://pith.science/paper/WEZWZR6E"},"agent_actions":{"view_html":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ","download_json":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ.json","view_paper":"https://pith.science/paper/WEZWZR6E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22493&json=true","fetch_graph":"https://pith.science/api/pith-number/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/graph.json","fetch_events":"https://pith.science/api/pith-number/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/action/storage_attestation","attest_author":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/action/author_attestation","sign_citation":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/action/citation_signature","submit_replication":"https://pith.science/pith/WEZWZR6ES5M5ZJJ6PL7KUGVFLJ/action/replication_record"}},"created_at":"2026-07-27T01:21:23.820978+00:00","updated_at":"2026-07-27T01:21:23.820978+00:00"}