{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EXYQBMGCNRCRPU7EC3Z6NA2ZCC","short_pith_number":"pith:EXYQBMGC","schema_version":"1.0","canonical_sha256":"25f100b0c26c4517d3e416f3e6835910a728134d6f1d048a7ba7f748725b042f","source":{"kind":"arxiv","id":"2410.23246","version":1},"attestation_state":"computed","paper":{"title":"Progression: an extrapolation principle for regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Gloria Buritic\\'a, Sebastian Engelke","submitted_at":"2024-10-30T17:29:51Z","abstract_excerpt":"The problem of regression extrapolation, or out-of-distribution generalization, arises when predictions are required at test points outside the range of the training data. In such cases, the non-parametric guarantees for regression methods from both statistics and machine learning typically fail. Based on the theory of tail dependence, we propose a novel statistical extrapolation principle. After a suitable, data-adaptive marginal transformation, it assumes a simple relationship between predictors and the response at the boundary of the training predictor samples. This assumption holds for a w"},"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":"2410.23246","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-10-30T17:29:51Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e4c25617b22d017bf8b4b7f1c850a19bcbf35bcfc4086bee599e63cf08509303","abstract_canon_sha256":"05e61d2c59794e4ee0c84b6d04b37ee5ceeb0881e939d738ec5e55ac4cd126e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:42.852761Z","signature_b64":"LYqnqHdKMfu4r0QUXWqxF3HkcSMdH8isckQpu+PIc14VMLui+5Bc2QGGbx7ytYm0LpRrTjGaQcrDPqbFBRV8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25f100b0c26c4517d3e416f3e6835910a728134d6f1d048a7ba7f748725b042f","last_reissued_at":"2026-07-05T09:28:42.852166Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:42.852166Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Progression: an extrapolation principle for regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Gloria Buritic\\'a, Sebastian Engelke","submitted_at":"2024-10-30T17:29:51Z","abstract_excerpt":"The problem of regression extrapolation, or out-of-distribution generalization, arises when predictions are required at test points outside the range of the training data. In such cases, the non-parametric guarantees for regression methods from both statistics and machine learning typically fail. Based on the theory of tail dependence, we propose a novel statistical extrapolation principle. After a suitable, data-adaptive marginal transformation, it assumes a simple relationship between predictors and the response at the boundary of the training predictor samples. This assumption holds for a w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23246","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/2410.23246/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":"2410.23246","created_at":"2026-07-05T09:28:42.852230+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.23246v1","created_at":"2026-07-05T09:28:42.852230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23246","created_at":"2026-07-05T09:28:42.852230+00:00"},{"alias_kind":"pith_short_12","alias_value":"EXYQBMGCNRCR","created_at":"2026-07-05T09:28:42.852230+00:00"},{"alias_kind":"pith_short_16","alias_value":"EXYQBMGCNRCRPU7E","created_at":"2026-07-05T09:28:42.852230+00:00"},{"alias_kind":"pith_short_8","alias_value":"EXYQBMGC","created_at":"2026-07-05T09:28:42.852230+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00265","citing_title":"Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach","ref_index":54,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01909","citing_title":"Extrapolation in Statistical Learning with Extreme Value Theory","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC","json":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC.json","graph_json":"https://pith.science/api/pith-number/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/graph.json","events_json":"https://pith.science/api/pith-number/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/events.json","paper":"https://pith.science/paper/EXYQBMGC"},"agent_actions":{"view_html":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC","download_json":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC.json","view_paper":"https://pith.science/paper/EXYQBMGC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.23246&json=true","fetch_graph":"https://pith.science/api/pith-number/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/graph.json","fetch_events":"https://pith.science/api/pith-number/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/action/storage_attestation","attest_author":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/action/author_attestation","sign_citation":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/action/citation_signature","submit_replication":"https://pith.science/pith/EXYQBMGCNRCRPU7EC3Z6NA2ZCC/action/replication_record"}},"created_at":"2026-07-05T09:28:42.852230+00:00","updated_at":"2026-07-05T09:28:42.852230+00:00"}