{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:PHQUQ3364Y4HEXOBID726LZ5JO","short_pith_number":"pith:PHQUQ336","schema_version":"1.0","canonical_sha256":"79e1486f7ee638725dc140ffaf2f3d4b97aaae2ea7c2dac7b862281a4ec495ff","source":{"kind":"arxiv","id":"2102.10080","version":2},"attestation_state":"computed","paper":{"title":"Distributed Bootstrap for Simultaneous Inference Under High Dimensionality","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Guang Cheng, Shih-Kang Chao, Yang Yu","submitted_at":"2021-02-19T18:28:29Z","abstract_excerpt":"We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces an $\\ell_\\infty$-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method at every iteration. We theoretically prove a lower bound on the number of communication rounds $\\tau_{\\min}$ that warrants the statistical accuracy and efficiency. Furthermore, $\\tau_{\\min}$ only increases logarithmically with the number of workers and the intrin"},"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":"2102.10080","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2021-02-19T18:28:29Z","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"title_canon_sha256":"2c1112d1a735681fa749d68f75ad71ad5b5384aaace161e5c19635c5b35b6c45","abstract_canon_sha256":"dcc28870a7815bb916443f6650d02ee5ec2bff8268cfa054ec7fee08c27a0c2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:31:20.520994Z","signature_b64":"Kds4sEnv7A501rLtSFoAqmtLg+OpCq42KAoVdEIffz81HbTCkbZxOWo2ht4ct5p8hzwzg1t/sIbjlWZbkyq1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79e1486f7ee638725dc140ffaf2f3d4b97aaae2ea7c2dac7b862281a4ec495ff","last_reissued_at":"2026-07-05T04:31:20.520499Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:31:20.520499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Bootstrap for Simultaneous Inference Under High Dimensionality","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Guang Cheng, Shih-Kang Chao, Yang Yu","submitted_at":"2021-02-19T18:28:29Z","abstract_excerpt":"We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces an $\\ell_\\infty$-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method at every iteration. We theoretically prove a lower bound on the number of communication rounds $\\tau_{\\min}$ that warrants the statistical accuracy and efficiency. Furthermore, $\\tau_{\\min}$ only increases logarithmically with the number of workers and the intrin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.10080","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/2102.10080/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":"2102.10080","created_at":"2026-07-05T04:31:20.520557+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.10080v2","created_at":"2026-07-05T04:31:20.520557+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.10080","created_at":"2026-07-05T04:31:20.520557+00:00"},{"alias_kind":"pith_short_12","alias_value":"PHQUQ3364Y4H","created_at":"2026-07-05T04:31:20.520557+00:00"},{"alias_kind":"pith_short_16","alias_value":"PHQUQ3364Y4HEXOB","created_at":"2026-07-05T04:31:20.520557+00:00"},{"alias_kind":"pith_short_8","alias_value":"PHQUQ336","created_at":"2026-07-05T04:31:20.520557+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/PHQUQ3364Y4HEXOBID726LZ5JO","json":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO.json","graph_json":"https://pith.science/api/pith-number/PHQUQ3364Y4HEXOBID726LZ5JO/graph.json","events_json":"https://pith.science/api/pith-number/PHQUQ3364Y4HEXOBID726LZ5JO/events.json","paper":"https://pith.science/paper/PHQUQ336"},"agent_actions":{"view_html":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO","download_json":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO.json","view_paper":"https://pith.science/paper/PHQUQ336","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.10080&json=true","fetch_graph":"https://pith.science/api/pith-number/PHQUQ3364Y4HEXOBID726LZ5JO/graph.json","fetch_events":"https://pith.science/api/pith-number/PHQUQ3364Y4HEXOBID726LZ5JO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO/action/storage_attestation","attest_author":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO/action/author_attestation","sign_citation":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO/action/citation_signature","submit_replication":"https://pith.science/pith/PHQUQ3364Y4HEXOBID726LZ5JO/action/replication_record"}},"created_at":"2026-07-05T04:31:20.520557+00:00","updated_at":"2026-07-05T04:31:20.520557+00:00"}