{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:H3CVUVQX32GIDX7YCS4ICJPZPD","short_pith_number":"pith:H3CVUVQX","schema_version":"1.0","canonical_sha256":"3ec55a5617de8c81dff814b88125f978d10a122c00ac614baa05e525df20e1bd","source":{"kind":"arxiv","id":"2210.04488","version":2},"attestation_state":"computed","paper":{"title":"Optimal Eigenvalue Shrinkage in the Semicircle Limit","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"David L. Donoho, Michael J. Feldman","submitted_at":"2022-10-10T08:33:05Z","abstract_excerpt":"Modern datasets are trending towards ever higher dimension. In response, recent theoretical studies of covariance estimation often assume the proportional-growth asymptotic framework, where the sample size $n$ and dimension $p$ are comparable, with $n, p \\rightarrow \\infty $ and $\\gamma_n = p/n \\rightarrow \\gamma > 0$. Yet, many datasets -- perhaps most -- have very different numbers of rows and columns. We consider instead the disproportional-growth asymptotic framework, where $n, p \\rightarrow \\infty$ and $\\gamma_n \\rightarrow 0$ or $\\gamma_n \\rightarrow \\infty$. Either disproportional limit"},"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":"2210.04488","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2022-10-10T08:33:05Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"3635f09e6390c2e510b3387940db8226158ab9c8db9d6750b502ac987c075fe2","abstract_canon_sha256":"3ec95ec274190089db6ee1ae9b0cff3aa27149183b4923a975626410147660fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:35:45.899290Z","signature_b64":"L1VwvDwZMK4ZCQ4vnTV/ClhFAOl+/v8cOmdgxANX+TfA4pEaZ1/dedgpFqN6JXnRyfWFQYmhnO9w0N5RLaCFDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ec55a5617de8c81dff814b88125f978d10a122c00ac614baa05e525df20e1bd","last_reissued_at":"2026-07-05T06:35:45.898809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:35:45.898809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Eigenvalue Shrinkage in the Semicircle Limit","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"David L. Donoho, Michael J. Feldman","submitted_at":"2022-10-10T08:33:05Z","abstract_excerpt":"Modern datasets are trending towards ever higher dimension. In response, recent theoretical studies of covariance estimation often assume the proportional-growth asymptotic framework, where the sample size $n$ and dimension $p$ are comparable, with $n, p \\rightarrow \\infty $ and $\\gamma_n = p/n \\rightarrow \\gamma > 0$. Yet, many datasets -- perhaps most -- have very different numbers of rows and columns. We consider instead the disproportional-growth asymptotic framework, where $n, p \\rightarrow \\infty$ and $\\gamma_n \\rightarrow 0$ or $\\gamma_n \\rightarrow \\infty$. Either disproportional limit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.04488","kind":"arxiv","version":2},"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/2210.04488/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":"2210.04488","created_at":"2026-07-05T06:35:45.898862+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.04488v2","created_at":"2026-07-05T06:35:45.898862+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.04488","created_at":"2026-07-05T06:35:45.898862+00:00"},{"alias_kind":"pith_short_12","alias_value":"H3CVUVQX32GI","created_at":"2026-07-05T06:35:45.898862+00:00"},{"alias_kind":"pith_short_16","alias_value":"H3CVUVQX32GIDX7Y","created_at":"2026-07-05T06:35:45.898862+00:00"},{"alias_kind":"pith_short_8","alias_value":"H3CVUVQX","created_at":"2026-07-05T06:35:45.898862+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09331","citing_title":"Dimension-Free Saddle-Point Escape in Muon","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD","json":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD.json","graph_json":"https://pith.science/api/pith-number/H3CVUVQX32GIDX7YCS4ICJPZPD/graph.json","events_json":"https://pith.science/api/pith-number/H3CVUVQX32GIDX7YCS4ICJPZPD/events.json","paper":"https://pith.science/paper/H3CVUVQX"},"agent_actions":{"view_html":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD","download_json":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD.json","view_paper":"https://pith.science/paper/H3CVUVQX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.04488&json=true","fetch_graph":"https://pith.science/api/pith-number/H3CVUVQX32GIDX7YCS4ICJPZPD/graph.json","fetch_events":"https://pith.science/api/pith-number/H3CVUVQX32GIDX7YCS4ICJPZPD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD/action/storage_attestation","attest_author":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD/action/author_attestation","sign_citation":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD/action/citation_signature","submit_replication":"https://pith.science/pith/H3CVUVQX32GIDX7YCS4ICJPZPD/action/replication_record"}},"created_at":"2026-07-05T06:35:45.898862+00:00","updated_at":"2026-07-05T06:35:45.898862+00:00"}