{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3UULUGYIZGQVFFLLPCBLXYBIDD","short_pith_number":"pith:3UULUGYI","schema_version":"1.0","canonical_sha256":"dd28ba1b08c9a152956b7882bbe02818f6a6d4c1508a20e93fcf48b81b067ee1","source":{"kind":"arxiv","id":"1811.05169","version":1},"attestation_state":"computed","paper":{"title":"Nonparametric geometric outlier detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Matias Heikkil\\\"a","submitted_at":"2018-11-13T08:59:41Z","abstract_excerpt":"Outlier detection is a major topic in robust statistics due to the high practical significance of anomalous observations. Many existing methods are, however, either parametric or cease to perform well when the data is far from linearly structured. In this paper, we propose a quantity, Delaunay outlyingness, that is a nonparametric outlyingness score applicable to data with complicated structure. The approach is based a well known triangulation of the sample, which seems to reflect the sparsity of the pointset to different directions in a useful way. In addition to appealing to heuristics, we d"},"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":"1811.05169","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-11-13T08:59:41Z","cross_cats_sorted":[],"title_canon_sha256":"5376647e12ee5ca504c34367a879acec0461c57532cb131e661c1415dcc83b85","abstract_canon_sha256":"b8af573fe824a97d4b3fdc6290dbc9ba065368c0b6e5700fc25fe9d9fa1f47ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:46.718180Z","signature_b64":"fFiP5xmK/sLOmvXNxiEkmz7lHYZY7AiBdh/20YMhs9eq5VbW4R9mfzsS/GvZ/LiMAXZeBQoEZ/zH4sSMKRvvBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd28ba1b08c9a152956b7882bbe02818f6a6d4c1508a20e93fcf48b81b067ee1","last_reissued_at":"2026-05-18T00:00:46.717769Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:46.717769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric geometric outlier detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Matias Heikkil\\\"a","submitted_at":"2018-11-13T08:59:41Z","abstract_excerpt":"Outlier detection is a major topic in robust statistics due to the high practical significance of anomalous observations. Many existing methods are, however, either parametric or cease to perform well when the data is far from linearly structured. In this paper, we propose a quantity, Delaunay outlyingness, that is a nonparametric outlyingness score applicable to data with complicated structure. The approach is based a well known triangulation of the sample, which seems to reflect the sparsity of the pointset to different directions in a useful way. In addition to appealing to heuristics, we d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05169","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1811.05169","created_at":"2026-05-18T00:00:46.717830+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.05169v1","created_at":"2026-05-18T00:00:46.717830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05169","created_at":"2026-05-18T00:00:46.717830+00:00"},{"alias_kind":"pith_short_12","alias_value":"3UULUGYIZGQV","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"3UULUGYIZGQVFFLL","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"3UULUGYI","created_at":"2026-05-18T12:32:05.422762+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/3UULUGYIZGQVFFLLPCBLXYBIDD","json":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD.json","graph_json":"https://pith.science/api/pith-number/3UULUGYIZGQVFFLLPCBLXYBIDD/graph.json","events_json":"https://pith.science/api/pith-number/3UULUGYIZGQVFFLLPCBLXYBIDD/events.json","paper":"https://pith.science/paper/3UULUGYI"},"agent_actions":{"view_html":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD","download_json":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD.json","view_paper":"https://pith.science/paper/3UULUGYI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.05169&json=true","fetch_graph":"https://pith.science/api/pith-number/3UULUGYIZGQVFFLLPCBLXYBIDD/graph.json","fetch_events":"https://pith.science/api/pith-number/3UULUGYIZGQVFFLLPCBLXYBIDD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD/action/storage_attestation","attest_author":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD/action/author_attestation","sign_citation":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD/action/citation_signature","submit_replication":"https://pith.science/pith/3UULUGYIZGQVFFLLPCBLXYBIDD/action/replication_record"}},"created_at":"2026-05-18T00:00:46.717830+00:00","updated_at":"2026-05-18T00:00:46.717830+00:00"}