{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2009:XQ3LDYUCULHTXOLTOYHOOJ7HDY","short_pith_number":"pith:XQ3LDYUC","schema_version":"1.0","canonical_sha256":"bc36b1e282a2cf3bb973760ee727e71e1db6ccae13a812c36e7d506993223b29","source":{"kind":"arxiv","id":"0906.1310","version":2},"attestation_state":"computed","paper":{"title":"Bootstrap consistency for general semiparametric $M$-estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Guang Cheng, Jianhua Z. Huang","submitted_at":"2009-06-06T21:23:08Z","abstract_excerpt":"Consider $M$-estimation in a semiparametric model that is characterized by a Euclidean parameter of interest and an infinite-dimensional nuisance parameter. As a general purpose approach to statistical inferences, the bootstrap has found wide applications in semiparametric $M$-estimation and, because of its simplicity, provides an attractive alternative to the inference approach based on the asymptotic distribution theory. The purpose of this paper is to provide theoretical justifications for the use of bootstrap as a semiparametric inferential tool. We show that, under general conditions, the"},"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":"0906.1310","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2009-06-06T21:23:08Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"448e83f4b1f7c07a8705f036dfec6dae8024174a7fcb52e48109643a39f7d7c8","abstract_canon_sha256":"8cf86476340dda44806ee18d36d8eac7618950a06f5d0216a45cce532cec694c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:30:04.708541Z","signature_b64":"0buk2+BPlsGXJ7cZkGEjYZgolTYZqSNnakiD9x0GzyeG+gQ6xOqQLFbm4V40ZtD1lg6YrLaM4VaA1IkCY53BDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc36b1e282a2cf3bb973760ee727e71e1db6ccae13a812c36e7d506993223b29","last_reissued_at":"2026-05-18T04:30:04.708150Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:30:04.708150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bootstrap consistency for general semiparametric $M$-estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Guang Cheng, Jianhua Z. Huang","submitted_at":"2009-06-06T21:23:08Z","abstract_excerpt":"Consider $M$-estimation in a semiparametric model that is characterized by a Euclidean parameter of interest and an infinite-dimensional nuisance parameter. As a general purpose approach to statistical inferences, the bootstrap has found wide applications in semiparametric $M$-estimation and, because of its simplicity, provides an attractive alternative to the inference approach based on the asymptotic distribution theory. The purpose of this paper is to provide theoretical justifications for the use of bootstrap as a semiparametric inferential tool. We show that, under general conditions, the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0906.1310","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"0906.1310","created_at":"2026-05-18T04:30:04.708211+00:00"},{"alias_kind":"arxiv_version","alias_value":"0906.1310v2","created_at":"2026-05-18T04:30:04.708211+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0906.1310","created_at":"2026-05-18T04:30:04.708211+00:00"},{"alias_kind":"pith_short_12","alias_value":"XQ3LDYUCULHT","created_at":"2026-05-18T12:26:02.257875+00:00"},{"alias_kind":"pith_short_16","alias_value":"XQ3LDYUCULHTXOLT","created_at":"2026-05-18T12:26:02.257875+00:00"},{"alias_kind":"pith_short_8","alias_value":"XQ3LDYUC","created_at":"2026-05-18T12:26:02.257875+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/XQ3LDYUCULHTXOLTOYHOOJ7HDY","json":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY.json","graph_json":"https://pith.science/api/pith-number/XQ3LDYUCULHTXOLTOYHOOJ7HDY/graph.json","events_json":"https://pith.science/api/pith-number/XQ3LDYUCULHTXOLTOYHOOJ7HDY/events.json","paper":"https://pith.science/paper/XQ3LDYUC"},"agent_actions":{"view_html":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY","download_json":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY.json","view_paper":"https://pith.science/paper/XQ3LDYUC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0906.1310&json=true","fetch_graph":"https://pith.science/api/pith-number/XQ3LDYUCULHTXOLTOYHOOJ7HDY/graph.json","fetch_events":"https://pith.science/api/pith-number/XQ3LDYUCULHTXOLTOYHOOJ7HDY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY/action/storage_attestation","attest_author":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY/action/author_attestation","sign_citation":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY/action/citation_signature","submit_replication":"https://pith.science/pith/XQ3LDYUCULHTXOLTOYHOOJ7HDY/action/replication_record"}},"created_at":"2026-05-18T04:30:04.708211+00:00","updated_at":"2026-05-18T04:30:04.708211+00:00"}