{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2ZTYLIX44ED23GNBU7YSYZLBZA","short_pith_number":"pith:2ZTYLIX4","schema_version":"1.0","canonical_sha256":"d66785a2fce107ad99a1a7f12c6561c8391e7279cad3522275d447f528552b89","source":{"kind":"arxiv","id":"2608.04800","version":1},"attestation_state":"computed","paper":{"title":"Debiasing the Lasso under Weaker Tail Assumptions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Leonardo Voltarelli, Roberto Imbuzeiro Oliveira","submitted_at":"2026-08-05T13:08:55Z","abstract_excerpt":"We consider the problem of high-dimensional inference with the lasso estimator. Different methods including 'double selection' techniques and multiple versions of the 'debiased lasso' have been proposed for this task with noticeable success. However, most guarantees assume strong hypotheses on the underlying data process and the errors in the linear regression model, such as subgaussian designs and independence between errors and the data itself. We show that 'standardizing' one's dataset -- a natural procedure in practical penalized regression -- leads to same results under much weaker hypoth"},"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":"2608.04800","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2026-08-05T13:08:55Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"31b631c8e0d01b4b52020faa0f551e5f76f33b7cb871b3c711d5d986372fb410","abstract_canon_sha256":"b1d556bb950ac4f45a2104e18b27848d83c64c90857a38f6ca9b0b050156e031"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-06T01:47:08.311132Z","signature_b64":"4PbCoViBRvbIQ9qmbcFtmaEXSgqdv8pFt2RVQR9/Ja9c7/MSUtFNVyheEDhAdgbZVTgO1jY7vEM4j5MGFlIrCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d66785a2fce107ad99a1a7f12c6561c8391e7279cad3522275d447f528552b89","last_reissued_at":"2026-08-06T01:47:08.309678Z","signature_status":"signed_v1","first_computed_at":"2026-08-06T01:47:08.309678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Debiasing the Lasso under Weaker Tail Assumptions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Leonardo Voltarelli, Roberto Imbuzeiro Oliveira","submitted_at":"2026-08-05T13:08:55Z","abstract_excerpt":"We consider the problem of high-dimensional inference with the lasso estimator. Different methods including 'double selection' techniques and multiple versions of the 'debiased lasso' have been proposed for this task with noticeable success. However, most guarantees assume strong hypotheses on the underlying data process and the errors in the linear regression model, such as subgaussian designs and independence between errors and the data itself. We show that 'standardizing' one's dataset -- a natural procedure in practical penalized regression -- leads to same results under much weaker hypoth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.04800","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/2608.04800/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":"2608.04800","created_at":"2026-08-06T01:47:08.311494+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.04800v1","created_at":"2026-08-06T01:47:08.311494+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.04800","created_at":"2026-08-06T01:47:08.311494+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZTYLIX44ED2","created_at":"2026-08-06T01:47:08.311494+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZTYLIX44ED23GNB","created_at":"2026-08-06T01:47:08.311494+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZTYLIX4","created_at":"2026-08-06T01:47:08.311494+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/2ZTYLIX44ED23GNBU7YSYZLBZA","json":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA.json","graph_json":"https://pith.science/api/pith-number/2ZTYLIX44ED23GNBU7YSYZLBZA/graph.json","events_json":"https://pith.science/api/pith-number/2ZTYLIX44ED23GNBU7YSYZLBZA/events.json","paper":"https://pith.science/paper/2ZTYLIX4"},"agent_actions":{"view_html":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA","download_json":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA.json","view_paper":"https://pith.science/paper/2ZTYLIX4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.04800&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZTYLIX44ED23GNBU7YSYZLBZA/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZTYLIX44ED23GNBU7YSYZLBZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA/action/storage_attestation","attest_author":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA/action/author_attestation","sign_citation":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA/action/citation_signature","submit_replication":"https://pith.science/pith/2ZTYLIX44ED23GNBU7YSYZLBZA/action/replication_record"}},"created_at":"2026-08-06T01:47:08.311494+00:00","updated_at":"2026-08-06T01:47:08.311494+00:00"}