{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:7ICTV6EKMNESL6PVXX4DEG6HRQ","short_pith_number":"pith:7ICTV6EK","canonical_record":{"source":{"id":"1906.06742","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-06-16T18:06:29Z","cross_cats_sorted":[],"title_canon_sha256":"17450905dffefe38a4a26f0b56286e46f8f8d15fa19dcfab70fef3fa053f6ce6","abstract_canon_sha256":"9f1bffcf79bf9373755a2a138bf9cfd621245cee0c8b189d4d3250ee44fac8ef"},"schema_version":"1.0"},"canonical_sha256":"fa053af88a634925f9f5bdf8321bc78c1c70c9bf6e2573d7d14d431f4ae3bd3e","source":{"kind":"arxiv","id":"1906.06742","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06742","created_at":"2026-05-17T23:43:12Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06742v1","created_at":"2026-05-17T23:43:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06742","created_at":"2026-05-17T23:43:12Z"},{"alias_kind":"pith_short_12","alias_value":"7ICTV6EKMNES","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"7ICTV6EKMNESL6PV","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"7ICTV6EK","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:7ICTV6EKMNESL6PVXX4DEG6HRQ","target":"record","payload":{"canonical_record":{"source":{"id":"1906.06742","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-06-16T18:06:29Z","cross_cats_sorted":[],"title_canon_sha256":"17450905dffefe38a4a26f0b56286e46f8f8d15fa19dcfab70fef3fa053f6ce6","abstract_canon_sha256":"9f1bffcf79bf9373755a2a138bf9cfd621245cee0c8b189d4d3250ee44fac8ef"},"schema_version":"1.0"},"canonical_sha256":"fa053af88a634925f9f5bdf8321bc78c1c70c9bf6e2573d7d14d431f4ae3bd3e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:12.786607Z","signature_b64":"Pllc6OJKJi6/Xpu09cM1E9QeyrGMvnMI0p3jZR1SZ/imuTtCVj/W9Zt4Hu19Ut7K/jiR4pJ8uq7D/d3OWQfDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa053af88a634925f9f5bdf8321bc78c1c70c9bf6e2573d7d14d431f4ae3bd3e","last_reissued_at":"2026-05-17T23:43:12.786062Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:12.786062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.06742","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/W5CeNNImev22B/Mw4GVcYzs2uOo3kSWVGYFlm16ImAclUBif63eZpGWD1WLd+yOChzE2/r3JWnMKRN6MUQ5Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T06:04:39.462991Z"},"content_sha256":"a3a6d6a15974bf7b0f95fd2f3cfa4509f8b1972082518d1b2db5570b3b02dee1","schema_version":"1.0","event_id":"sha256:a3a6d6a15974bf7b0f95fd2f3cfa4509f8b1972082518d1b2db5570b3b02dee1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:7ICTV6EKMNESL6PVXX4DEG6HRQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Depth-based Weighted Jackknife Empirical Likelihood for Non-smooth U-structure Equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Xin Dang, Yichuan Zhao, Yongli Sang","submitted_at":"2019-06-16T18:06:29Z","abstract_excerpt":"In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with $U$-statistic structure. Jackknife empirical likelihood (JEL) approach can solve this problem efficiently by reducing the computation complexity of the empirical likelihood (EL) method. However, as EL, JEL suffers the sensitivity problem to outliers. In this paper, we propose a weighted jackknife empirical likelihood (WJEL) to tackle the above limitation of JEL. The proposed WJEL tilts the JEL function by assigning smaller weights to outliers. The asymptotic of the WJEL ratio statist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06742","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tTyV6DYy1fiSn8X6JFlVY/HUsQ3nbBdCejrnoxDVH0XS5afPYC3SfEHE84J4dqKdhhzyLluC06p0HwOnSIi/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T06:04:39.463336Z"},"content_sha256":"356dc7e896ab024ba513e3128df1b3230555eeaade9bb27d1a162abd06441fe9","schema_version":"1.0","event_id":"sha256:356dc7e896ab024ba513e3128df1b3230555eeaade9bb27d1a162abd06441fe9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7ICTV6EKMNESL6PVXX4DEG6HRQ/bundle.json","state_url":"https://pith.science/pith/7ICTV6EKMNESL6PVXX4DEG6HRQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7ICTV6EKMNESL6PVXX4DEG6HRQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T06:04:39Z","links":{"resolver":"https://pith.science/pith/7ICTV6EKMNESL6PVXX4DEG6HRQ","bundle":"https://pith.science/pith/7ICTV6EKMNESL6PVXX4DEG6HRQ/bundle.json","state":"https://pith.science/pith/7ICTV6EKMNESL6PVXX4DEG6HRQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7ICTV6EKMNESL6PVXX4DEG6HRQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7ICTV6EKMNESL6PVXX4DEG6HRQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9f1bffcf79bf9373755a2a138bf9cfd621245cee0c8b189d4d3250ee44fac8ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-06-16T18:06:29Z","title_canon_sha256":"17450905dffefe38a4a26f0b56286e46f8f8d15fa19dcfab70fef3fa053f6ce6"},"schema_version":"1.0","source":{"id":"1906.06742","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.06742","created_at":"2026-05-17T23:43:12Z"},{"alias_kind":"arxiv_version","alias_value":"1906.06742v1","created_at":"2026-05-17T23:43:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06742","created_at":"2026-05-17T23:43:12Z"},{"alias_kind":"pith_short_12","alias_value":"7ICTV6EKMNES","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"7ICTV6EKMNESL6PV","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"7ICTV6EK","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:356dc7e896ab024ba513e3128df1b3230555eeaade9bb27d1a162abd06441fe9","target":"graph","created_at":"2026-05-17T23:43:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with $U$-statistic structure. Jackknife empirical likelihood (JEL) approach can solve this problem efficiently by reducing the computation complexity of the empirical likelihood (EL) method. However, as EL, JEL suffers the sensitivity problem to outliers. In this paper, we propose a weighted jackknife empirical likelihood (WJEL) to tackle the above limitation of JEL. The proposed WJEL tilts the JEL function by assigning smaller weights to outliers. The asymptotic of the WJEL ratio statist","authors_text":"Xin Dang, Yichuan Zhao, Yongli Sang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-06-16T18:06:29Z","title":"Depth-based Weighted Jackknife Empirical Likelihood for Non-smooth U-structure Equations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06742","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a3a6d6a15974bf7b0f95fd2f3cfa4509f8b1972082518d1b2db5570b3b02dee1","target":"record","created_at":"2026-05-17T23:43:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9f1bffcf79bf9373755a2a138bf9cfd621245cee0c8b189d4d3250ee44fac8ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-06-16T18:06:29Z","title_canon_sha256":"17450905dffefe38a4a26f0b56286e46f8f8d15fa19dcfab70fef3fa053f6ce6"},"schema_version":"1.0","source":{"id":"1906.06742","kind":"arxiv","version":1}},"canonical_sha256":"fa053af88a634925f9f5bdf8321bc78c1c70c9bf6e2573d7d14d431f4ae3bd3e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fa053af88a634925f9f5bdf8321bc78c1c70c9bf6e2573d7d14d431f4ae3bd3e","first_computed_at":"2026-05-17T23:43:12.786062Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:12.786062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pllc6OJKJi6/Xpu09cM1E9QeyrGMvnMI0p3jZR1SZ/imuTtCVj/W9Zt4Hu19Ut7K/jiR4pJ8uq7D/d3OWQfDCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:12.786607Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.06742","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a3a6d6a15974bf7b0f95fd2f3cfa4509f8b1972082518d1b2db5570b3b02dee1","sha256:356dc7e896ab024ba513e3128df1b3230555eeaade9bb27d1a162abd06441fe9"],"state_sha256":"4f306eacde21e0effa2c3444d62dbb5b3d9b1c92ef33761c8059987f56de7e83"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OGI+QOspeyM2vuAIFoZxVuoUJ4t/SmyFqLJZy9RschSR7bKFOh3n8HI2k61lzBEKoiGDtl/t1FnjRhu5K28fCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T06:04:39.465145Z","bundle_sha256":"c54689af88f7b04796a2152e74f73abd96565d2ac2ffaa4202d0fe4b2d12aa3c"}}