{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MATR5NMOFZA2MNPXUMIWSL4LHU","short_pith_number":"pith:MATR5NMO","canonical_record":{"source":{"id":"2505.19925","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-05-26T12:46:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8e6c9254ad6b190bc16d0d239865c1ae9d2d782020b8b447a5e312a96da36bbb","abstract_canon_sha256":"18ee3a20463983227b38cc7dfdd8a7f982866be177e933b1e1db8d2e68ea2b7e"},"schema_version":"1.0"},"canonical_sha256":"60271eb58e2e41a635f7a311692f8b3d0f5b1c345141e8b010a4940445cbb701","source":{"kind":"arxiv","id":"2505.19925","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19925","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19925v2","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19925","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"pith_short_12","alias_value":"MATR5NMOFZA2","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"pith_short_16","alias_value":"MATR5NMOFZA2MNPX","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"pith_short_8","alias_value":"MATR5NMO","created_at":"2026-06-02T02:04:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MATR5NMOFZA2MNPXUMIWSL4LHU","target":"record","payload":{"canonical_record":{"source":{"id":"2505.19925","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-05-26T12:46:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8e6c9254ad6b190bc16d0d239865c1ae9d2d782020b8b447a5e312a96da36bbb","abstract_canon_sha256":"18ee3a20463983227b38cc7dfdd8a7f982866be177e933b1e1db8d2e68ea2b7e"},"schema_version":"1.0"},"canonical_sha256":"60271eb58e2e41a635f7a311692f8b3d0f5b1c345141e8b010a4940445cbb701","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:06.300370Z","signature_b64":"7iCY6+KjcBuXD+XBhvC+7RPijNcSwpmeRneox5awWz3vtj9a44jbqi+7KeP6YUQKS0tqnkezpevsIrVdx0ORDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60271eb58e2e41a635f7a311692f8b3d0f5b1c345141e8b010a4940445cbb701","last_reissued_at":"2026-06-02T02:04:06.299954Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:06.299954Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.19925","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:04:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jJeYzEK7YtyNw/iuRg1LPYr+70f3oJ19r/IfqGOCrtLh0FmauPi6Uj1dYlvOUMDN45PN8+fDt6alg96KUm/1BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:01:28.654424Z"},"content_sha256":"ec40d0a6acc0134ea97a3d138a5c73ca7749d789895273e0e872d3010c1c1e7c","schema_version":"1.0","event_id":"sha256:ec40d0a6acc0134ea97a3d138a5c73ca7749d789895273e0e872d3010c1c1e7c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MATR5NMOFZA2MNPXUMIWSL4LHU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cellwise and Casewise Robust Covariance in High Dimensions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ME","authors_text":"Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw","submitted_at":"2025-05-26T12:46:44Z","abstract_excerpt":"The sample covariance matrix is a cornerstone of multivariate statistics, but it is highly sensitive to outliers. These can be casewise outliers, such as cases belonging to a different population, or cellwise outliers, which are deviating cells (entries) of the data matrix. Recently some robust covariance estimators have been developed that can handle both types of outliers, but their computation is only feasible up to at most 20 dimensions. To remedy this we propose the cellRCov method, a robust covariance estimator that simultaneously handles casewise outliers, cellwise outliers, and missing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19925","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.19925/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":"2026-06-02T02:04:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Khzxf3myaLdssb35+M/LS2pZtty5oa8Zps9AZwHUJO6ngGWdqzzJOZ9NHNsYXyAIRZEFiQqjzWQT6ZeMIkzQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T16:01:28.654799Z"},"content_sha256":"ecaf7ec9d56443ba5b1ece6495376fc020616f5a5c7f20b1b1b2595df51a224c","schema_version":"1.0","event_id":"sha256:ecaf7ec9d56443ba5b1ece6495376fc020616f5a5c7f20b1b1b2595df51a224c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MATR5NMOFZA2MNPXUMIWSL4LHU/bundle.json","state_url":"https://pith.science/pith/MATR5NMOFZA2MNPXUMIWSL4LHU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MATR5NMOFZA2MNPXUMIWSL4LHU/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":"2026-06-06T16:01:28Z","links":{"resolver":"https://pith.science/pith/MATR5NMOFZA2MNPXUMIWSL4LHU","bundle":"https://pith.science/pith/MATR5NMOFZA2MNPXUMIWSL4LHU/bundle.json","state":"https://pith.science/pith/MATR5NMOFZA2MNPXUMIWSL4LHU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MATR5NMOFZA2MNPXUMIWSL4LHU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MATR5NMOFZA2MNPXUMIWSL4LHU","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":"18ee3a20463983227b38cc7dfdd8a7f982866be177e933b1e1db8d2e68ea2b7e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-05-26T12:46:44Z","title_canon_sha256":"8e6c9254ad6b190bc16d0d239865c1ae9d2d782020b8b447a5e312a96da36bbb"},"schema_version":"1.0","source":{"id":"2505.19925","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19925","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19925v2","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19925","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"pith_short_12","alias_value":"MATR5NMOFZA2","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"pith_short_16","alias_value":"MATR5NMOFZA2MNPX","created_at":"2026-06-02T02:04:06Z"},{"alias_kind":"pith_short_8","alias_value":"MATR5NMO","created_at":"2026-06-02T02:04:06Z"}],"graph_snapshots":[{"event_id":"sha256:ecaf7ec9d56443ba5b1ece6495376fc020616f5a5c7f20b1b1b2595df51a224c","target":"graph","created_at":"2026-06-02T02:04:06Z","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/2505.19925/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The sample covariance matrix is a cornerstone of multivariate statistics, but it is highly sensitive to outliers. These can be casewise outliers, such as cases belonging to a different population, or cellwise outliers, which are deviating cells (entries) of the data matrix. Recently some robust covariance estimators have been developed that can handle both types of outliers, but their computation is only feasible up to at most 20 dimensions. To remedy this we propose the cellRCov method, a robust covariance estimator that simultaneously handles casewise outliers, cellwise outliers, and missing","authors_text":"Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-05-26T12:46:44Z","title":"Cellwise and Casewise Robust Covariance in High Dimensions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19925","kind":"arxiv","version":2},"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:ec40d0a6acc0134ea97a3d138a5c73ca7749d789895273e0e872d3010c1c1e7c","target":"record","created_at":"2026-06-02T02:04:06Z","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":"18ee3a20463983227b38cc7dfdd8a7f982866be177e933b1e1db8d2e68ea2b7e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-05-26T12:46:44Z","title_canon_sha256":"8e6c9254ad6b190bc16d0d239865c1ae9d2d782020b8b447a5e312a96da36bbb"},"schema_version":"1.0","source":{"id":"2505.19925","kind":"arxiv","version":2}},"canonical_sha256":"60271eb58e2e41a635f7a311692f8b3d0f5b1c345141e8b010a4940445cbb701","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"60271eb58e2e41a635f7a311692f8b3d0f5b1c345141e8b010a4940445cbb701","first_computed_at":"2026-06-02T02:04:06.299954Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:04:06.299954Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7iCY6+KjcBuXD+XBhvC+7RPijNcSwpmeRneox5awWz3vtj9a44jbqi+7KeP6YUQKS0tqnkezpevsIrVdx0ORDA==","signature_status":"signed_v1","signed_at":"2026-06-02T02:04:06.300370Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.19925","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec40d0a6acc0134ea97a3d138a5c73ca7749d789895273e0e872d3010c1c1e7c","sha256:ecaf7ec9d56443ba5b1ece6495376fc020616f5a5c7f20b1b1b2595df51a224c"],"state_sha256":"215588cc249a469df982a5e154347610aa57d89291377c5b119e03002c04e121"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FAqOshkAD48nRKDf67g7hnj5BHzIPY+/NSwQcSFGOyi+eJO7ScxNuR1AecqilSmdJsGuerjzCew91LsPbxvIAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T16:01:28.656942Z","bundle_sha256":"adf012593ae55f57e7282c7dc6fe2e70b2342aeaa5d66699b1b9310e3dfb28d4"}}