{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PDZAJAYBK24XHZ2XQCHR5252SS","short_pith_number":"pith:PDZAJAYB","schema_version":"1.0","canonical_sha256":"78f204830156b973e757808f1eebba9487f219f25fdb5e3032c01645583d5414","source":{"kind":"arxiv","id":"1903.03810","version":1},"attestation_state":"computed","paper":{"title":"Distributed Feature Screening via Componentwise Debiasing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chen Xu, Runze Li, Xingxiang Li, Zhiming Xia","submitted_at":"2019-03-09T14:32:21Z","abstract_excerpt":"Feature screening is a powerful tool in the analysis of high dimensional data. When the sample size $N$ and the number of features $p$ are both large, the implementation of classic screening methods can be numerically challenging. In this paper, we propose a distributed screening framework for big data setup. In the spirit of \"divide-and-conquer\", the proposed framework expresses a correlation measure as a function of several component parameters, each of which can be distributively estimated using a natural U-statistic from data segments. With the component estimates aggregated, we obtain a f"},"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":"1903.03810","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-03-09T14:32:21Z","cross_cats_sorted":[],"title_canon_sha256":"a3de67cb1bad5c26830dc2329bab49860af11b79cdfec0d29f403a3dc3bbae98","abstract_canon_sha256":"eac67d30ca53560c24d0cea07b354828b44c4bf458668109889b0a265e7ee489"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:40.888717Z","signature_b64":"nHnm7yOKKO5Coyk8O7LtJVYz+D3wTvBlGsTVHj6USUJGjxbfye/dTggkTiVTZIDajhN41WfdsfOjClBbm2QkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78f204830156b973e757808f1eebba9487f219f25fdb5e3032c01645583d5414","last_reissued_at":"2026-05-17T23:51:40.888130Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:40.888130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Feature Screening via Componentwise Debiasing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chen Xu, Runze Li, Xingxiang Li, Zhiming Xia","submitted_at":"2019-03-09T14:32:21Z","abstract_excerpt":"Feature screening is a powerful tool in the analysis of high dimensional data. When the sample size $N$ and the number of features $p$ are both large, the implementation of classic screening methods can be numerically challenging. In this paper, we propose a distributed screening framework for big data setup. In the spirit of \"divide-and-conquer\", the proposed framework expresses a correlation measure as a function of several component parameters, each of which can be distributively estimated using a natural U-statistic from data segments. With the component estimates aggregated, we obtain a f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.03810","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":"1903.03810","created_at":"2026-05-17T23:51:40.888215+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.03810v1","created_at":"2026-05-17T23:51:40.888215+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.03810","created_at":"2026-05-17T23:51:40.888215+00:00"},{"alias_kind":"pith_short_12","alias_value":"PDZAJAYBK24X","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PDZAJAYBK24XHZ2X","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PDZAJAYB","created_at":"2026-05-18T12:33:24.271573+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/PDZAJAYBK24XHZ2XQCHR5252SS","json":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS.json","graph_json":"https://pith.science/api/pith-number/PDZAJAYBK24XHZ2XQCHR5252SS/graph.json","events_json":"https://pith.science/api/pith-number/PDZAJAYBK24XHZ2XQCHR5252SS/events.json","paper":"https://pith.science/paper/PDZAJAYB"},"agent_actions":{"view_html":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS","download_json":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS.json","view_paper":"https://pith.science/paper/PDZAJAYB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.03810&json=true","fetch_graph":"https://pith.science/api/pith-number/PDZAJAYBK24XHZ2XQCHR5252SS/graph.json","fetch_events":"https://pith.science/api/pith-number/PDZAJAYBK24XHZ2XQCHR5252SS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS/action/storage_attestation","attest_author":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS/action/author_attestation","sign_citation":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS/action/citation_signature","submit_replication":"https://pith.science/pith/PDZAJAYBK24XHZ2XQCHR5252SS/action/replication_record"}},"created_at":"2026-05-17T23:51:40.888215+00:00","updated_at":"2026-05-17T23:51:40.888215+00:00"}