{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:ZUSDWGVZHI7RV4F5ZTBFGSD3IP","short_pith_number":"pith:ZUSDWGVZ","canonical_record":{"source":{"id":"1306.5461","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-06-23T19:28:20Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"312bb5ade7a28d2b99325e090eefaec8dd7ef78239828211496cd243bd1e9e49","abstract_canon_sha256":"f2da1090f8f4ddd42a1345567cec451f7df4ebced625c1af3d034f2ae3ce3c49"},"schema_version":"1.0"},"canonical_sha256":"cd243b1ab93a3f1af0bdccc253487b43ec55ea463059ba58237ab5e9bf10e75b","source":{"kind":"arxiv","id":"1306.5461","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.5461","created_at":"2026-05-18T03:20:08Z"},{"alias_kind":"arxiv_version","alias_value":"1306.5461v1","created_at":"2026-05-18T03:20:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.5461","created_at":"2026-05-18T03:20:08Z"},{"alias_kind":"pith_short_12","alias_value":"ZUSDWGVZHI7R","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_16","alias_value":"ZUSDWGVZHI7RV4F5","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_8","alias_value":"ZUSDWGVZ","created_at":"2026-05-18T12:28:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:ZUSDWGVZHI7RV4F5ZTBFGSD3IP","target":"record","payload":{"canonical_record":{"source":{"id":"1306.5461","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-06-23T19:28:20Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"312bb5ade7a28d2b99325e090eefaec8dd7ef78239828211496cd243bd1e9e49","abstract_canon_sha256":"f2da1090f8f4ddd42a1345567cec451f7df4ebced625c1af3d034f2ae3ce3c49"},"schema_version":"1.0"},"canonical_sha256":"cd243b1ab93a3f1af0bdccc253487b43ec55ea463059ba58237ab5e9bf10e75b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:20:08.043247Z","signature_b64":"BFBjEWfupKmRUyOMXKZ7APFkWALZ0BcKrPH+kxwjsfRaerKkYsDLSmaBGAXLPqrDVae6PsZmSH+fQ3JGmjhzBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd243b1ab93a3f1af0bdccc253487b43ec55ea463059ba58237ab5e9bf10e75b","last_reissued_at":"2026-05-18T03:20:08.042545Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:20:08.042545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1306.5461","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":"2026-05-18T03:20:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/OrMcaNS5RhOzIqmaX9XAP3+U0CmWll5WpHXITCqo5TppQ9RBpiowqYJKd81+nN/5SszjhgYThaRY4plZBYEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T10:29:48.142739Z"},"content_sha256":"75c09a104f6b7f88db726b3db27960470d4c6834ad795cc28eb5fb46f5be824a","schema_version":"1.0","event_id":"sha256:75c09a104f6b7f88db726b3db27960470d4c6834ad795cc28eb5fb46f5be824a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:ZUSDWGVZHI7RV4F5ZTBFGSD3IP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Connections between Semiparametrics and Robustness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Helmut Rieder","submitted_at":"2013-06-23T19:28:20Z","abstract_excerpt":"Robust and semiparametric statistics are of the same historical origin and largely employ the same locally asymptotically normal framework. In our talk, we consider he following more intrinsic connections of both fields:\n  1) Robust influence curves for semiparametric models with infinite dimensional nuisance parameter; for example, for semiparametric regression (Cox), and mixture models (Neyman--Scott).\n  2) Adaptiveness in the sense of Stein's necessary condition of robust neighborhood models and estimators with respect to a finite dimensional nuisance parameter; for example, location, linea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.5461","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":"2026-05-18T03:20:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gBJZgI0P4obPjpa9r9mtY2ivMz/VxazTILIt79OXjOOGW/nt7b3qEP/eU88ESL3ZUxstwIg67cE3aitEhzFvAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T10:29:48.143097Z"},"content_sha256":"775543850562aaf24c81f7a3bfbd04d5ea15a5a6e35e2a06e2ba4b838c1726bf","schema_version":"1.0","event_id":"sha256:775543850562aaf24c81f7a3bfbd04d5ea15a5a6e35e2a06e2ba4b838c1726bf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZUSDWGVZHI7RV4F5ZTBFGSD3IP/bundle.json","state_url":"https://pith.science/pith/ZUSDWGVZHI7RV4F5ZTBFGSD3IP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZUSDWGVZHI7RV4F5ZTBFGSD3IP/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":"2026-06-02T10:29:48Z","links":{"resolver":"https://pith.science/pith/ZUSDWGVZHI7RV4F5ZTBFGSD3IP","bundle":"https://pith.science/pith/ZUSDWGVZHI7RV4F5ZTBFGSD3IP/bundle.json","state":"https://pith.science/pith/ZUSDWGVZHI7RV4F5ZTBFGSD3IP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZUSDWGVZHI7RV4F5ZTBFGSD3IP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:ZUSDWGVZHI7RV4F5ZTBFGSD3IP","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":"f2da1090f8f4ddd42a1345567cec451f7df4ebced625c1af3d034f2ae3ce3c49","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-06-23T19:28:20Z","title_canon_sha256":"312bb5ade7a28d2b99325e090eefaec8dd7ef78239828211496cd243bd1e9e49"},"schema_version":"1.0","source":{"id":"1306.5461","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.5461","created_at":"2026-05-18T03:20:08Z"},{"alias_kind":"arxiv_version","alias_value":"1306.5461v1","created_at":"2026-05-18T03:20:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.5461","created_at":"2026-05-18T03:20:08Z"},{"alias_kind":"pith_short_12","alias_value":"ZUSDWGVZHI7R","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_16","alias_value":"ZUSDWGVZHI7RV4F5","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_8","alias_value":"ZUSDWGVZ","created_at":"2026-05-18T12:28:09Z"}],"graph_snapshots":[{"event_id":"sha256:775543850562aaf24c81f7a3bfbd04d5ea15a5a6e35e2a06e2ba4b838c1726bf","target":"graph","created_at":"2026-05-18T03:20:08Z","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":"Robust and semiparametric statistics are of the same historical origin and largely employ the same locally asymptotically normal framework. In our talk, we consider he following more intrinsic connections of both fields:\n  1) Robust influence curves for semiparametric models with infinite dimensional nuisance parameter; for example, for semiparametric regression (Cox), and mixture models (Neyman--Scott).\n  2) Adaptiveness in the sense of Stein's necessary condition of robust neighborhood models and estimators with respect to a finite dimensional nuisance parameter; for example, location, linea","authors_text":"Helmut Rieder","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-06-23T19:28:20Z","title":"Connections between Semiparametrics and Robustness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.5461","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":"sha256:75c09a104f6b7f88db726b3db27960470d4c6834ad795cc28eb5fb46f5be824a","target":"record","created_at":"2026-05-18T03:20:08Z","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":"f2da1090f8f4ddd42a1345567cec451f7df4ebced625c1af3d034f2ae3ce3c49","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-06-23T19:28:20Z","title_canon_sha256":"312bb5ade7a28d2b99325e090eefaec8dd7ef78239828211496cd243bd1e9e49"},"schema_version":"1.0","source":{"id":"1306.5461","kind":"arxiv","version":1}},"canonical_sha256":"cd243b1ab93a3f1af0bdccc253487b43ec55ea463059ba58237ab5e9bf10e75b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd243b1ab93a3f1af0bdccc253487b43ec55ea463059ba58237ab5e9bf10e75b","first_computed_at":"2026-05-18T03:20:08.042545Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:20:08.042545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BFBjEWfupKmRUyOMXKZ7APFkWALZ0BcKrPH+kxwjsfRaerKkYsDLSmaBGAXLPqrDVae6PsZmSH+fQ3JGmjhzBA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:20:08.043247Z","signed_message":"canonical_sha256_bytes"},"source_id":"1306.5461","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75c09a104f6b7f88db726b3db27960470d4c6834ad795cc28eb5fb46f5be824a","sha256:775543850562aaf24c81f7a3bfbd04d5ea15a5a6e35e2a06e2ba4b838c1726bf"],"state_sha256":"5019574aaaad1176d1f6ac96c6f0bf72b4de6ab6b0b2e7d2942f01d82d5e93d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ieEDrP5rDSjtMi7IiDp353d2L8ss5wpYVoreQpIf0N38uFu5xl6BpTXhb4xfjpHImsZrvciNYAf2xIEmV8McDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T10:29:48.145239Z","bundle_sha256":"89722e9ceb01639d98ff2f6eaec5610f422ad25d672121b6bd45667d6be45a2a"}}