{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IM5PISF6GRQEMAMOQGD2BAA7KX","short_pith_number":"pith:IM5PISF6","schema_version":"1.0","canonical_sha256":"433af448be346046018e8187a0801f55f24c74820f342b082d6482604cd7438a","source":{"kind":"arxiv","id":"2109.14856","version":1},"attestation_state":"computed","paper":{"title":"Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.CO","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Bingyuan Liu, Jian Kang, Lingzhou Xue, Peter X.K. Song, Qi Zhang","submitted_at":"2021-09-30T05:29:54Z","abstract_excerpt":"It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses. We propose a new robust high-dimensional regression with coefficient thresholding, in which an efficient nonconvex estimation procedure is proposed through a thresholding function and the robust Huber loss. The proposed regularization method accounts for complex dependence structures in predictors and is robust against outliers in outcomes. Theoretically, we analyze rigorously the landsc"},"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":"2109.14856","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2021-09-30T05:29:54Z","cross_cats_sorted":["math.ST","stat.CO","stat.ML","stat.TH"],"title_canon_sha256":"2d4bbaf4315c51f4ae7b1894ce4f38f7915dba11c7213930d18577b95efadee2","abstract_canon_sha256":"9492a367e4834e4575551a262f1ccc43f4a9d9234cdc352c90b6318ce18c5dba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:18:45.039518Z","signature_b64":"IXFo1iRlA/uUvJoJlQ5Kec9QmZ2Jlyc3NlIdIaEifpHYA/V7irEXsTf1sdgrjOcFc9P8JH5yOl+grk9zN01fAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"433af448be346046018e8187a0801f55f24c74820f342b082d6482604cd7438a","last_reissued_at":"2026-07-05T03:18:45.039044Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:18:45.039044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.CO","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Bingyuan Liu, Jian Kang, Lingzhou Xue, Peter X.K. Song, Qi Zhang","submitted_at":"2021-09-30T05:29:54Z","abstract_excerpt":"It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses. We propose a new robust high-dimensional regression with coefficient thresholding, in which an efficient nonconvex estimation procedure is proposed through a thresholding function and the robust Huber loss. The proposed regularization method accounts for complex dependence structures in predictors and is robust against outliers in outcomes. Theoretically, we analyze rigorously the landsc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.14856","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/2109.14856/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":"2109.14856","created_at":"2026-07-05T03:18:45.039099+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.14856v1","created_at":"2026-07-05T03:18:45.039099+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.14856","created_at":"2026-07-05T03:18:45.039099+00:00"},{"alias_kind":"pith_short_12","alias_value":"IM5PISF6GRQE","created_at":"2026-07-05T03:18:45.039099+00:00"},{"alias_kind":"pith_short_16","alias_value":"IM5PISF6GRQEMAMO","created_at":"2026-07-05T03:18:45.039099+00:00"},{"alias_kind":"pith_short_8","alias_value":"IM5PISF6","created_at":"2026-07-05T03:18:45.039099+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/IM5PISF6GRQEMAMOQGD2BAA7KX","json":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX.json","graph_json":"https://pith.science/api/pith-number/IM5PISF6GRQEMAMOQGD2BAA7KX/graph.json","events_json":"https://pith.science/api/pith-number/IM5PISF6GRQEMAMOQGD2BAA7KX/events.json","paper":"https://pith.science/paper/IM5PISF6"},"agent_actions":{"view_html":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX","download_json":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX.json","view_paper":"https://pith.science/paper/IM5PISF6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.14856&json=true","fetch_graph":"https://pith.science/api/pith-number/IM5PISF6GRQEMAMOQGD2BAA7KX/graph.json","fetch_events":"https://pith.science/api/pith-number/IM5PISF6GRQEMAMOQGD2BAA7KX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX/action/storage_attestation","attest_author":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX/action/author_attestation","sign_citation":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX/action/citation_signature","submit_replication":"https://pith.science/pith/IM5PISF6GRQEMAMOQGD2BAA7KX/action/replication_record"}},"created_at":"2026-07-05T03:18:45.039099+00:00","updated_at":"2026-07-05T03:18:45.039099+00:00"}