{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IKQO7SFWMRWMSLUHBFEYHNKEQI","short_pith_number":"pith:IKQO7SFW","schema_version":"1.0","canonical_sha256":"42a0efc8b6646cc92e87094983b5448232470b1630a29fd0398f73ee98cad719","source":{"kind":"arxiv","id":"2605.20325","version":1},"attestation_state":"computed","paper":{"title":"Explainable Outlier Detection for Multivariate Functional Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Horst Lewitschnig, Marcus Mayrhofer, Peter Filzmoser, Una Radoji\\v{c}i\\'c","submitted_at":"2026-05-19T18:00:01Z","abstract_excerpt":"This work addresses the challenges of robust covariance estimation and interpretable outlier detection for multivariate functional data with separable covariance structure. We develop a method that simultaneously improves robustness and interpretability in this context by establishing a connection between stochastic processes with separable covariance structures and the corresponding matrix-variate distribution of their basis representations. Leveraging this connection, we employ the recently developed matrix-variate counterpart of the Minimum Covariance Determinant estimator (MMCD) in conjunc"},"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":"2605.20325","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T18:00:01Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"488c11267ae14114369ae705a47ba849555807f8ff9545c6fe857e042ba1cdd3","abstract_canon_sha256":"ea425d5be8f69396ff6d0e7166dbc41f51da21a57e4721625f3660cb3d151698"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T00:04:25.400354Z","signature_b64":"F+JBbYy7E9Z19ySk0/ZGf/YIW8Oyfs2WkhXjt0qt70NuDbS0l/JPAcU2h3ey2B6Ofypa+rXkKKdblvbkhcmdAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42a0efc8b6646cc92e87094983b5448232470b1630a29fd0398f73ee98cad719","last_reissued_at":"2026-05-21T00:04:25.399860Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T00:04:25.399860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainable Outlier Detection for Multivariate Functional Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Horst Lewitschnig, Marcus Mayrhofer, Peter Filzmoser, Una Radoji\\v{c}i\\'c","submitted_at":"2026-05-19T18:00:01Z","abstract_excerpt":"This work addresses the challenges of robust covariance estimation and interpretable outlier detection for multivariate functional data with separable covariance structure. We develop a method that simultaneously improves robustness and interpretability in this context by establishing a connection between stochastic processes with separable covariance structures and the corresponding matrix-variate distribution of their basis representations. Leveraging this connection, we employ the recently developed matrix-variate counterpart of the Minimum Covariance Determinant estimator (MMCD) in conjunc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20325","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/2605.20325/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.20325","created_at":"2026-05-21T00:04:25.399934+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.20325v1","created_at":"2026-05-21T00:04:25.399934+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20325","created_at":"2026-05-21T00:04:25.399934+00:00"},{"alias_kind":"pith_short_12","alias_value":"IKQO7SFWMRWM","created_at":"2026-05-21T00:04:25.399934+00:00"},{"alias_kind":"pith_short_16","alias_value":"IKQO7SFWMRWMSLUH","created_at":"2026-05-21T00:04:25.399934+00:00"},{"alias_kind":"pith_short_8","alias_value":"IKQO7SFW","created_at":"2026-05-21T00:04:25.399934+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/IKQO7SFWMRWMSLUHBFEYHNKEQI","json":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI.json","graph_json":"https://pith.science/api/pith-number/IKQO7SFWMRWMSLUHBFEYHNKEQI/graph.json","events_json":"https://pith.science/api/pith-number/IKQO7SFWMRWMSLUHBFEYHNKEQI/events.json","paper":"https://pith.science/paper/IKQO7SFW"},"agent_actions":{"view_html":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI","download_json":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI.json","view_paper":"https://pith.science/paper/IKQO7SFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.20325&json=true","fetch_graph":"https://pith.science/api/pith-number/IKQO7SFWMRWMSLUHBFEYHNKEQI/graph.json","fetch_events":"https://pith.science/api/pith-number/IKQO7SFWMRWMSLUHBFEYHNKEQI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI/action/storage_attestation","attest_author":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI/action/author_attestation","sign_citation":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI/action/citation_signature","submit_replication":"https://pith.science/pith/IKQO7SFWMRWMSLUHBFEYHNKEQI/action/replication_record"}},"created_at":"2026-05-21T00:04:25.399934+00:00","updated_at":"2026-05-21T00:04:25.399934+00:00"}