{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:BAWO3K4PTIDYPC37UCYWIVBR5W","short_pith_number":"pith:BAWO3K4P","schema_version":"1.0","canonical_sha256":"082cedab8f9a07878b7fa0b1645431ed8437221aea9781213f5cb8067cb76f3c","source":{"kind":"arxiv","id":"1602.05801","version":1},"attestation_state":"computed","paper":{"title":"Leave-one-out prediction intervals in linear regression models with many variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Hannes Leeb, Lukas Steinberger","submitted_at":"2016-02-18T13:52:17Z","abstract_excerpt":"We study prediction intervals based on leave-one-out residuals in a linear regression model where the number of explanatory variables can be large compared to sample size. We establish uniform asymptotic validity (conditional on the training sample) of the proposed interval under minimal assumptions on the unknown error distribution and the high dimensional design. Our intervals are generic in the sense that they are valid for a large class of linear predictors used to obtain a point forecast, such as robust M-estimators, James-Stein type estimators and penalized estimators like the LASSO. The"},"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":"1602.05801","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-02-18T13:52:17Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"b399aa2523c102f30d84de620cb36f034573debe91c02ad56f503c60e6af7b51","abstract_canon_sha256":"4b5e933ca4f497c791e3ee8dc7b1a297834eb1b814e80b035e308ac00031abbd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:23.179637Z","signature_b64":"L9bSCC9gx8uoFX3FEWK/JZmNeZLCZPJHaIrcT8dejsb7VUhDegCWci164qipDHqaGKOh5e4emJ6nI5NZBb4WBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"082cedab8f9a07878b7fa0b1645431ed8437221aea9781213f5cb8067cb76f3c","last_reissued_at":"2026-05-18T01:20:23.178904Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:23.178904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leave-one-out prediction intervals in linear regression models with many variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Hannes Leeb, Lukas Steinberger","submitted_at":"2016-02-18T13:52:17Z","abstract_excerpt":"We study prediction intervals based on leave-one-out residuals in a linear regression model where the number of explanatory variables can be large compared to sample size. We establish uniform asymptotic validity (conditional on the training sample) of the proposed interval under minimal assumptions on the unknown error distribution and the high dimensional design. Our intervals are generic in the sense that they are valid for a large class of linear predictors used to obtain a point forecast, such as robust M-estimators, James-Stein type estimators and penalized estimators like the LASSO. The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.05801","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":"1602.05801","created_at":"2026-05-18T01:20:23.179010+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.05801v1","created_at":"2026-05-18T01:20:23.179010+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.05801","created_at":"2026-05-18T01:20:23.179010+00:00"},{"alias_kind":"pith_short_12","alias_value":"BAWO3K4PTIDY","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_16","alias_value":"BAWO3K4PTIDYPC37","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_8","alias_value":"BAWO3K4P","created_at":"2026-05-18T12:30:07.202191+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/BAWO3K4PTIDYPC37UCYWIVBR5W","json":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W.json","graph_json":"https://pith.science/api/pith-number/BAWO3K4PTIDYPC37UCYWIVBR5W/graph.json","events_json":"https://pith.science/api/pith-number/BAWO3K4PTIDYPC37UCYWIVBR5W/events.json","paper":"https://pith.science/paper/BAWO3K4P"},"agent_actions":{"view_html":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W","download_json":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W.json","view_paper":"https://pith.science/paper/BAWO3K4P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.05801&json=true","fetch_graph":"https://pith.science/api/pith-number/BAWO3K4PTIDYPC37UCYWIVBR5W/graph.json","fetch_events":"https://pith.science/api/pith-number/BAWO3K4PTIDYPC37UCYWIVBR5W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W/action/storage_attestation","attest_author":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W/action/author_attestation","sign_citation":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W/action/citation_signature","submit_replication":"https://pith.science/pith/BAWO3K4PTIDYPC37UCYWIVBR5W/action/replication_record"}},"created_at":"2026-05-18T01:20:23.179010+00:00","updated_at":"2026-05-18T01:20:23.179010+00:00"}