{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YGBJK25BYA3T2TRSOZVA6P4DNP","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":"be9f5189c32b59674bc56c837f265a4daedc95352cb82f715274d2ff75f4aa57","cross_cats_sorted":["math.PR","stat.ME","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-29T14:24:49Z","title_canon_sha256":"0f240c14f1f8330880d89dd1ca829659b8db5ad125c8be97b03f6de467eee54b"},"schema_version":"1.0","source":{"id":"2403.20200","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.20200","created_at":"2026-05-20T02:05:31Z"},{"alias_kind":"arxiv_version","alias_value":"2403.20200v5","created_at":"2026-05-20T02:05:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.20200","created_at":"2026-05-20T02:05:31Z"},{"alias_kind":"pith_short_12","alias_value":"YGBJK25BYA3T","created_at":"2026-05-20T02:05:31Z"},{"alias_kind":"pith_short_16","alias_value":"YGBJK25BYA3T2TRS","created_at":"2026-05-20T02:05:31Z"},{"alias_kind":"pith_short_8","alias_value":"YGBJK25B","created_at":"2026-05-20T02:05:31Z"}],"graph_snapshots":[{"event_id":"sha256:612f7245b43430fec21d66244841e5066eb921b1dbc472b6c732405f577de99e","target":"graph","created_at":"2026-05-20T02:05:31Z","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/2403.20200/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-dimensional linear regression has been thoroughly studied in the context of independent and identically distributed data. We propose to investigate high-dimensional regression models for independent but non-identically distributed data. To this end, we suppose that the set of observed predictors (or features) is a random matrix with a variance profile and with dimensions growing at a proportional rate. Assuming a random effect model, we study the predictive risk of the ridge estimator for linear regression with such a variance profile. In this setting, we provide deterministic equivalents","authors_text":"Camille Male, Issa-Mbenard Dabo, J\\'er\\'emie Bigot","cross_cats":["math.PR","stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-29T14:24:49Z","title":"High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.20200","kind":"arxiv","version":5},"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":"sha256:3cec24abb50546ce835282f8b0e9651feff4b20ba45be0ecac034a2c85f4cbea","target":"record","created_at":"2026-05-20T02:05:31Z","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":"be9f5189c32b59674bc56c837f265a4daedc95352cb82f715274d2ff75f4aa57","cross_cats_sorted":["math.PR","stat.ME","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-29T14:24:49Z","title_canon_sha256":"0f240c14f1f8330880d89dd1ca829659b8db5ad125c8be97b03f6de467eee54b"},"schema_version":"1.0","source":{"id":"2403.20200","kind":"arxiv","version":5}},"canonical_sha256":"c182956ba1c0373d4e32766a0f3f836bd849161d3981736415cc3685e8e442fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c182956ba1c0373d4e32766a0f3f836bd849161d3981736415cc3685e8e442fc","first_computed_at":"2026-05-20T02:05:31.595559Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T02:05:31.595559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sNKnqeRxb8co6sEPeBSoQJP9TWbvk0Wfu8CCLbT0mtA4Gxfpxhio6V4XIb1x5F1T4dig/CxyIC/sc/WqQ5EgCA==","signature_status":"signed_v1","signed_at":"2026-05-20T02:05:31.596275Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.20200","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3cec24abb50546ce835282f8b0e9651feff4b20ba45be0ecac034a2c85f4cbea","sha256:612f7245b43430fec21d66244841e5066eb921b1dbc472b6c732405f577de99e"],"state_sha256":"a0c5effa62d5ffab676fe45da8137f5745e1bc0d323a62d7070912a0585745f4"}