{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:53NM5X6TRWLRUEBAMQB72DJATO","short_pith_number":"pith:53NM5X6T","canonical_record":{"source":{"id":"2504.13018","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-17T15:25:51Z","cross_cats_sorted":[],"title_canon_sha256":"47248a9b4ae555a1de0d309a8a2b55bb2ad3b620375e98fdad19d5677277fc72","abstract_canon_sha256":"d8421779b066414aff60bb5b3c0277ebe35f49ecedc5ca0f52b5e33fe56c2db0"},"schema_version":"1.0"},"canonical_sha256":"eedacedfd38d971a10206403fd0d209b9a0ff338e46f92dc5349c907b3d363e3","source":{"kind":"arxiv","id":"2504.13018","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13018","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13018v1","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13018","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"pith_short_12","alias_value":"53NM5X6TRWLR","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"pith_short_16","alias_value":"53NM5X6TRWLRUEBA","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"pith_short_8","alias_value":"53NM5X6T","created_at":"2026-07-05T10:50:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:53NM5X6TRWLRUEBAMQB72DJATO","target":"record","payload":{"canonical_record":{"source":{"id":"2504.13018","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-17T15:25:51Z","cross_cats_sorted":[],"title_canon_sha256":"47248a9b4ae555a1de0d309a8a2b55bb2ad3b620375e98fdad19d5677277fc72","abstract_canon_sha256":"d8421779b066414aff60bb5b3c0277ebe35f49ecedc5ca0f52b5e33fe56c2db0"},"schema_version":"1.0"},"canonical_sha256":"eedacedfd38d971a10206403fd0d209b9a0ff338e46f92dc5349c907b3d363e3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:36.237296Z","signature_b64":"6iu10pHci7pdLUApa0681UQS2aMYlEzVh0G5Puik0x2r+iqIIpEtA03aYZ0L/lKbFVuC4OwFoh9N7xcZa7MlDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eedacedfd38d971a10206403fd0d209b9a0ff338e46f92dc5349c907b3d363e3","last_reissued_at":"2026-07-05T10:50:36.236763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:36.236763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.13018","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":"2026-07-05T10:50:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o0RjowdCf6zGCaIqYUEEMf70MhqSvf5FsbZJykU2v/ErE6Ox2Qo2AaNiLs0veHzqHHwvLSvNY4PscFBC2lYqCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T14:12:34.998198Z"},"content_sha256":"b4274f7e4c6f8bfa910945a7413c237022f61b4a5351bb3b46c53f6fcb910873","schema_version":"1.0","event_id":"sha256:b4274f7e4c6f8bfa910945a7413c237022f61b4a5351bb3b46c53f6fcb910873"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:53NM5X6TRWLRUEBAMQB72DJATO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High Dimensional Sparse Canonical Correlation Analysis for Elliptical Symmetric Distributions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chengde Qian, Long Feng, Yanhong Liu","submitted_at":"2025-04-17T15:25:51Z","abstract_excerpt":"This paper proposes a robust high-dimensional sparse canonical correlation analysis (CCA) method for investigating linear relationships between two high-dimensional random vectors, focusing on elliptical symmetric distributions. Traditional CCA methods, based on sample covariance matrices, struggle in high-dimensional settings, particularly when data exhibit heavy-tailed distributions. To address this, we introduce the spatial-sign covariance matrix as a robust estimator, combined with a sparsity-inducing penalty to efficiently estimate canonical correlations. Theoretical analysis shows that o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13018","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/2504.13018/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"peL1NgaUixauFs7S1SEj1IffdRDHk6MEjpTgbWzbbYMrAulwFfDmaWAFK/h1YoPtznioHzclJOmddoDmIWwMCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T14:12:34.999030Z"},"content_sha256":"739d4879c303677eb4a770fbcaa694e0c8ce38200cb40478c172c8b4340c3545","schema_version":"1.0","event_id":"sha256:739d4879c303677eb4a770fbcaa694e0c8ce38200cb40478c172c8b4340c3545"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/53NM5X6TRWLRUEBAMQB72DJATO/bundle.json","state_url":"https://pith.science/pith/53NM5X6TRWLRUEBAMQB72DJATO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/53NM5X6TRWLRUEBAMQB72DJATO/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":"2026-08-20T14:12:35Z","links":{"resolver":"https://pith.science/pith/53NM5X6TRWLRUEBAMQB72DJATO","bundle":"https://pith.science/pith/53NM5X6TRWLRUEBAMQB72DJATO/bundle.json","state":"https://pith.science/pith/53NM5X6TRWLRUEBAMQB72DJATO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/53NM5X6TRWLRUEBAMQB72DJATO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:53NM5X6TRWLRUEBAMQB72DJATO","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":"d8421779b066414aff60bb5b3c0277ebe35f49ecedc5ca0f52b5e33fe56c2db0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-17T15:25:51Z","title_canon_sha256":"47248a9b4ae555a1de0d309a8a2b55bb2ad3b620375e98fdad19d5677277fc72"},"schema_version":"1.0","source":{"id":"2504.13018","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13018","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13018v1","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13018","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"pith_short_12","alias_value":"53NM5X6TRWLR","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"pith_short_16","alias_value":"53NM5X6TRWLRUEBA","created_at":"2026-07-05T10:50:36Z"},{"alias_kind":"pith_short_8","alias_value":"53NM5X6T","created_at":"2026-07-05T10:50:36Z"}],"graph_snapshots":[{"event_id":"sha256:739d4879c303677eb4a770fbcaa694e0c8ce38200cb40478c172c8b4340c3545","target":"graph","created_at":"2026-07-05T10:50:36Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.13018/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper proposes a robust high-dimensional sparse canonical correlation analysis (CCA) method for investigating linear relationships between two high-dimensional random vectors, focusing on elliptical symmetric distributions. Traditional CCA methods, based on sample covariance matrices, struggle in high-dimensional settings, particularly when data exhibit heavy-tailed distributions. To address this, we introduce the spatial-sign covariance matrix as a robust estimator, combined with a sparsity-inducing penalty to efficiently estimate canonical correlations. Theoretical analysis shows that o","authors_text":"Chengde Qian, Long Feng, Yanhong Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-17T15:25:51Z","title":"High Dimensional Sparse Canonical Correlation Analysis for Elliptical Symmetric Distributions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13018","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:b4274f7e4c6f8bfa910945a7413c237022f61b4a5351bb3b46c53f6fcb910873","target":"record","created_at":"2026-07-05T10:50:36Z","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":"d8421779b066414aff60bb5b3c0277ebe35f49ecedc5ca0f52b5e33fe56c2db0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-17T15:25:51Z","title_canon_sha256":"47248a9b4ae555a1de0d309a8a2b55bb2ad3b620375e98fdad19d5677277fc72"},"schema_version":"1.0","source":{"id":"2504.13018","kind":"arxiv","version":1}},"canonical_sha256":"eedacedfd38d971a10206403fd0d209b9a0ff338e46f92dc5349c907b3d363e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eedacedfd38d971a10206403fd0d209b9a0ff338e46f92dc5349c907b3d363e3","first_computed_at":"2026-07-05T10:50:36.236763Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:36.236763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6iu10pHci7pdLUApa0681UQS2aMYlEzVh0G5Puik0x2r+iqIIpEtA03aYZ0L/lKbFVuC4OwFoh9N7xcZa7MlDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:36.237296Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.13018","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b4274f7e4c6f8bfa910945a7413c237022f61b4a5351bb3b46c53f6fcb910873","sha256:739d4879c303677eb4a770fbcaa694e0c8ce38200cb40478c172c8b4340c3545"],"state_sha256":"bccb393a35b115872bbd0b705d3e86df4ea1088baf58eb979fcfe5ca5e20db45"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A6MMsJlVVw+5mLwTlCOVv3THn6+RFPuPALiVo3NHEqUUUDVX3wpTokO/Cplb3bnLbSpX9NSjtYGJgaGnFFuiBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T14:12:35.009222Z","bundle_sha256":"6bb8b6874a4208e1513ee7c2253754b7cbaf65b32a2144d9c6ae116b77b63768"}}