{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HDOYTBHHMMSGUX75JJTU3SF3HT","short_pith_number":"pith:HDOYTBHH","schema_version":"1.0","canonical_sha256":"38dd8984e763246a5ffd4a674dc8bb3cd74e773d0e9711abe5ca7d4ad180255b","source":{"kind":"arxiv","id":"1705.02162","version":1},"attestation_state":"computed","paper":{"title":"A New Sparse and Robust Adaptive Lasso Estimator for the Independent Contamination Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Abdelhak M. Zoubir, Bastian Alt, Jasin Machkour, Michael Muma","submitted_at":"2017-05-05T10:39:29Z","abstract_excerpt":"Many problems in signal processing require finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. When dealing with real-world data, the presence of outliers and impulsive noise must also be accounted for. In past decades, the vast majority of robust linear regression estimators has focused on robustness against rowwise contamination. Even so called `high breakdown' estimators rely on the assumption that a majority of rows of the regression matrix is not affected by outliers. Only very recently, the first cellwise robust regression estimation methods hav"},"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":"1705.02162","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-05-05T10:39:29Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"6f3d62f45242bcb51d0d4160ff67be4816100daac4cc28d8af3abad9bb52e1cc","abstract_canon_sha256":"88fd4121544566abf6e2f736d5bbecd84613a5994e0b2d877cb8a19cefd9f61d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:00.503907Z","signature_b64":"KkgOMJplOYLme9gJWr2f8o2lted6hZtqic6HYiEli4abOQAk9XZN7iiTGY3+CYbXIrADyi+DHVFovt+3IuCbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38dd8984e763246a5ffd4a674dc8bb3cd74e773d0e9711abe5ca7d4ad180255b","last_reissued_at":"2026-05-18T00:45:00.503462Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:00.503462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A New Sparse and Robust Adaptive Lasso Estimator for the Independent Contamination Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Abdelhak M. Zoubir, Bastian Alt, Jasin Machkour, Michael Muma","submitted_at":"2017-05-05T10:39:29Z","abstract_excerpt":"Many problems in signal processing require finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. When dealing with real-world data, the presence of outliers and impulsive noise must also be accounted for. In past decades, the vast majority of robust linear regression estimators has focused on robustness against rowwise contamination. Even so called `high breakdown' estimators rely on the assumption that a majority of rows of the regression matrix is not affected by outliers. Only very recently, the first cellwise robust regression estimation methods hav"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02162","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":"1705.02162","created_at":"2026-05-18T00:45:00.503535+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.02162v1","created_at":"2026-05-18T00:45:00.503535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02162","created_at":"2026-05-18T00:45:00.503535+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDOYTBHHMMSG","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDOYTBHHMMSGUX75","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDOYTBHH","created_at":"2026-05-18T12:31:18.294218+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/HDOYTBHHMMSGUX75JJTU3SF3HT","json":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT.json","graph_json":"https://pith.science/api/pith-number/HDOYTBHHMMSGUX75JJTU3SF3HT/graph.json","events_json":"https://pith.science/api/pith-number/HDOYTBHHMMSGUX75JJTU3SF3HT/events.json","paper":"https://pith.science/paper/HDOYTBHH"},"agent_actions":{"view_html":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT","download_json":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT.json","view_paper":"https://pith.science/paper/HDOYTBHH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.02162&json=true","fetch_graph":"https://pith.science/api/pith-number/HDOYTBHHMMSGUX75JJTU3SF3HT/graph.json","fetch_events":"https://pith.science/api/pith-number/HDOYTBHHMMSGUX75JJTU3SF3HT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT/action/storage_attestation","attest_author":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT/action/author_attestation","sign_citation":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT/action/citation_signature","submit_replication":"https://pith.science/pith/HDOYTBHHMMSGUX75JJTU3SF3HT/action/replication_record"}},"created_at":"2026-05-18T00:45:00.503535+00:00","updated_at":"2026-05-18T00:45:00.503535+00:00"}