{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VTQSW35HW2LBWEHTRDQHQZVVDQ","short_pith_number":"pith:VTQSW35H","schema_version":"1.0","canonical_sha256":"ace12b6fa7b6961b10f388e07866b51c0432921cfa2636d97b0f519338fddfb7","source":{"kind":"arxiv","id":"2411.02909","version":1},"attestation_state":"computed","paper":{"title":"When is it worthwhile to jackknife? Breaking the quadratic barrier for Z-estimators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Fangzhou Su, Licong Lin, Martin Wainwright, Peng Ding, Wenlong Mou","submitted_at":"2024-11-05T08:48:49Z","abstract_excerpt":"Resampling methods are especially well-suited to inference with estimators that provide only \"black-box'' access. Jackknife is a form of resampling, widely used for bias correction and variance estimation, that is well-understood under classical scaling where the sample size $n$ grows for a fixed problem. We study its behavior in application to estimating functionals using high-dimensional $Z$-estimators, allowing both the sample size $n$ and problem dimension $d$ to diverge. We begin showing that the plug-in estimator based on the $Z$-estimate suffers from a quadratic breakdown: while it is $"},"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":"2411.02909","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-11-05T08:48:49Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"7fc4eda8051dbd086e6117f5136be19da8ff222b06b8a5b063b31de86ff72a76","abstract_canon_sha256":"09dfadb8a3d5f6e211ee419b37c8c96320cd2a2b740bc38d3e816833d98087f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:31:20.057000Z","signature_b64":"e4PS1KjQRWAZbj1jTb+m2WeBXOTQ8MntcjF2cJd+79TkU1977IkCl8j8/NkWPATB1kXgtsJv7FH0XxQSEXUoBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ace12b6fa7b6961b10f388e07866b51c0432921cfa2636d97b0f519338fddfb7","last_reissued_at":"2026-07-05T09:31:20.056638Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:31:20.056638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When is it worthwhile to jackknife? Breaking the quadratic barrier for Z-estimators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Fangzhou Su, Licong Lin, Martin Wainwright, Peng Ding, Wenlong Mou","submitted_at":"2024-11-05T08:48:49Z","abstract_excerpt":"Resampling methods are especially well-suited to inference with estimators that provide only \"black-box'' access. Jackknife is a form of resampling, widely used for bias correction and variance estimation, that is well-understood under classical scaling where the sample size $n$ grows for a fixed problem. We study its behavior in application to estimating functionals using high-dimensional $Z$-estimators, allowing both the sample size $n$ and problem dimension $d$ to diverge. We begin showing that the plug-in estimator based on the $Z$-estimate suffers from a quadratic breakdown: while it is $"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02909","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/2411.02909/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":"2411.02909","created_at":"2026-07-05T09:31:20.056695+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.02909v1","created_at":"2026-07-05T09:31:20.056695+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02909","created_at":"2026-07-05T09:31:20.056695+00:00"},{"alias_kind":"pith_short_12","alias_value":"VTQSW35HW2LB","created_at":"2026-07-05T09:31:20.056695+00:00"},{"alias_kind":"pith_short_16","alias_value":"VTQSW35HW2LBWEHT","created_at":"2026-07-05T09:31:20.056695+00:00"},{"alias_kind":"pith_short_8","alias_value":"VTQSW35H","created_at":"2026-07-05T09:31:20.056695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01706","citing_title":"Higher-Order Debiased Estimators for General Treatment Models","ref_index":80,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ","json":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ.json","graph_json":"https://pith.science/api/pith-number/VTQSW35HW2LBWEHTRDQHQZVVDQ/graph.json","events_json":"https://pith.science/api/pith-number/VTQSW35HW2LBWEHTRDQHQZVVDQ/events.json","paper":"https://pith.science/paper/VTQSW35H"},"agent_actions":{"view_html":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ","download_json":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ.json","view_paper":"https://pith.science/paper/VTQSW35H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.02909&json=true","fetch_graph":"https://pith.science/api/pith-number/VTQSW35HW2LBWEHTRDQHQZVVDQ/graph.json","fetch_events":"https://pith.science/api/pith-number/VTQSW35HW2LBWEHTRDQHQZVVDQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ/action/storage_attestation","attest_author":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ/action/author_attestation","sign_citation":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ/action/citation_signature","submit_replication":"https://pith.science/pith/VTQSW35HW2LBWEHTRDQHQZVVDQ/action/replication_record"}},"created_at":"2026-07-05T09:31:20.056695+00:00","updated_at":"2026-07-05T09:31:20.056695+00:00"}