{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:2KBSJ4HD67FMMJRMGVMRTP555T","short_pith_number":"pith:2KBSJ4HD","schema_version":"1.0","canonical_sha256":"d28324f0e3f7cac6262c355919bfbdecd3d6974909d6fcd265ca3f85d6249fd5","source":{"kind":"arxiv","id":"1603.04093","version":1},"attestation_state":"computed","paper":{"title":"Adjusted Jackknife Empirical Likelihood","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Wei Ning, Ying-Ju Chen","submitted_at":"2016-03-13T23:21:52Z","abstract_excerpt":"Jackknife empirical likelihood (JEL) is an effective modified version of empirical likelihood method (EL). Through the construction of the jackknife pseudo-values, JEL overcomes the computational difficulty of EL method when its constraints are nonlinear while maintaining the same asymptotic results for one sample and two-sample U statistics. In this paper, we propose an adjusted version of JEL to guarantee that the adjusted jackknife empirical likelihood (AJEL) statistic is well-defined for all the values of the parameter, instead of restricting on the convex hull of the estimation equation. "},"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":"1603.04093","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-13T23:21:52Z","cross_cats_sorted":[],"title_canon_sha256":"6680bde725d3cd0c5122e82d3d497ec5819bc6addc1de882c22751bfe385eb67","abstract_canon_sha256":"fa9dfb4da75caa485d17cdd3a0c7092b4dccb1fd287db66b8aee9a4d6dd3815e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:09.468774Z","signature_b64":"9B+a+fne+SDYlY3L624/xSWKqQtmsZ/e/70xg1x6yqRfhkr6EIUTD1PbAzhdEX7lmibd7oIu1ZQOktgrIwuIBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d28324f0e3f7cac6262c355919bfbdecd3d6974909d6fcd265ca3f85d6249fd5","last_reissued_at":"2026-05-18T01:19:09.468143Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:09.468143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adjusted Jackknife Empirical Likelihood","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Wei Ning, Ying-Ju Chen","submitted_at":"2016-03-13T23:21:52Z","abstract_excerpt":"Jackknife empirical likelihood (JEL) is an effective modified version of empirical likelihood method (EL). Through the construction of the jackknife pseudo-values, JEL overcomes the computational difficulty of EL method when its constraints are nonlinear while maintaining the same asymptotic results for one sample and two-sample U statistics. In this paper, we propose an adjusted version of JEL to guarantee that the adjusted jackknife empirical likelihood (AJEL) statistic is well-defined for all the values of the parameter, instead of restricting on the convex hull of the estimation equation. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.04093","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":""},"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":"1603.04093","created_at":"2026-05-18T01:19:09.468230+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.04093v1","created_at":"2026-05-18T01:19:09.468230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.04093","created_at":"2026-05-18T01:19:09.468230+00:00"},{"alias_kind":"pith_short_12","alias_value":"2KBSJ4HD67FM","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"2KBSJ4HD67FMMJRM","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"2KBSJ4HD","created_at":"2026-05-18T12:29:55.572404+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/2KBSJ4HD67FMMJRMGVMRTP555T","json":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T.json","graph_json":"https://pith.science/api/pith-number/2KBSJ4HD67FMMJRMGVMRTP555T/graph.json","events_json":"https://pith.science/api/pith-number/2KBSJ4HD67FMMJRMGVMRTP555T/events.json","paper":"https://pith.science/paper/2KBSJ4HD"},"agent_actions":{"view_html":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T","download_json":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T.json","view_paper":"https://pith.science/paper/2KBSJ4HD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.04093&json=true","fetch_graph":"https://pith.science/api/pith-number/2KBSJ4HD67FMMJRMGVMRTP555T/graph.json","fetch_events":"https://pith.science/api/pith-number/2KBSJ4HD67FMMJRMGVMRTP555T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T/action/storage_attestation","attest_author":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T/action/author_attestation","sign_citation":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T/action/citation_signature","submit_replication":"https://pith.science/pith/2KBSJ4HD67FMMJRMGVMRTP555T/action/replication_record"}},"created_at":"2026-05-18T01:19:09.468230+00:00","updated_at":"2026-05-18T01:19:09.468230+00:00"}