{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:W7SQXMEEXTWZHFSFZNIUUN2I3V","short_pith_number":"pith:W7SQXMEE","schema_version":"1.0","canonical_sha256":"b7e50bb084bced939645cb514a3748dd439096135bdeb0e26c50793b80e64f53","source":{"kind":"arxiv","id":"1906.11043","version":1},"attestation_state":"computed","paper":{"title":"Principal Component Analysis for Multivariate Extremes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Anne Sabourin (LTCI), Holger Drees","submitted_at":"2019-06-26T12:44:54Z","abstract_excerpt":"The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of this measure is concentrated on a lower dimensional subspace, meaning that certain linear combinations of the components are much likelier to be large than others. Identifying this subspace and thus reducing the dimension will facilitate a refined statistical analysis. In this work we apply Principal Component Analysis (PCA) to a re-scaled version of radially"},"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":"1906.11043","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-06-26T12:44:54Z","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"71b3bf5c0549da4ad1da0d776bed4795377c3282968b4c111bdf9d7f31a1cd2c","abstract_canon_sha256":"f0a88de8c5e57d378f31ea3fa035d1af6008e9d5716dab295150ee80d03f45e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:10.097804Z","signature_b64":"/s2+RNKX+oUy2CS+fFKKtoL14mwilo2ftWlYkhF5cOJQNqH9LQRtC5hxJha/ynEXCWuJOUAUDo6UQ4yMfmyjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7e50bb084bced939645cb514a3748dd439096135bdeb0e26c50793b80e64f53","last_reissued_at":"2026-05-17T23:42:10.097175Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:10.097175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Principal Component Analysis for Multivariate Extremes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Anne Sabourin (LTCI), Holger Drees","submitted_at":"2019-06-26T12:44:54Z","abstract_excerpt":"The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of this measure is concentrated on a lower dimensional subspace, meaning that certain linear combinations of the components are much likelier to be large than others. Identifying this subspace and thus reducing the dimension will facilitate a refined statistical analysis. In this work we apply Principal Component Analysis (PCA) to a re-scaled version of radially"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11043","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":"1906.11043","created_at":"2026-05-17T23:42:10.097274+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.11043v1","created_at":"2026-05-17T23:42:10.097274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11043","created_at":"2026-05-17T23:42:10.097274+00:00"},{"alias_kind":"pith_short_12","alias_value":"W7SQXMEEXTWZ","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"W7SQXMEEXTWZHFSF","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"W7SQXMEE","created_at":"2026-05-18T12:33:30.264802+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/W7SQXMEEXTWZHFSFZNIUUN2I3V","json":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V.json","graph_json":"https://pith.science/api/pith-number/W7SQXMEEXTWZHFSFZNIUUN2I3V/graph.json","events_json":"https://pith.science/api/pith-number/W7SQXMEEXTWZHFSFZNIUUN2I3V/events.json","paper":"https://pith.science/paper/W7SQXMEE"},"agent_actions":{"view_html":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V","download_json":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V.json","view_paper":"https://pith.science/paper/W7SQXMEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.11043&json=true","fetch_graph":"https://pith.science/api/pith-number/W7SQXMEEXTWZHFSFZNIUUN2I3V/graph.json","fetch_events":"https://pith.science/api/pith-number/W7SQXMEEXTWZHFSFZNIUUN2I3V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V/action/storage_attestation","attest_author":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V/action/author_attestation","sign_citation":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V/action/citation_signature","submit_replication":"https://pith.science/pith/W7SQXMEEXTWZHFSFZNIUUN2I3V/action/replication_record"}},"created_at":"2026-05-17T23:42:10.097274+00:00","updated_at":"2026-05-17T23:42:10.097274+00:00"}