{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:NUVVB5OLATB27ZV5LCQP4KHLGS","short_pith_number":"pith:NUVVB5OL","schema_version":"1.0","canonical_sha256":"6d2b50f5cb04c3afe6bd58a0fe28eb348701f57f7e17cf7b3a62f38a9d70d1eb","source":{"kind":"arxiv","id":"1506.02222","version":5},"attestation_state":"computed","paper":{"title":"No penalty no tears: Least squares in high-dimensional linear models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Chenlei Leng, David Dunson, Xiangyu Wang","submitted_at":"2015-06-07T05:45:24Z","abstract_excerpt":"Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size. For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step algorithms involving least squares fitting and hard thresholding. The algorithms are methodologically simple to understand intuitively, computationally easy to implement efficiently, and theoretically appealing for choosing models consistently. Numerical exercises comparing our methods with penalizati"},"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":"1506.02222","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-06-07T05:45:24Z","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"4b5bd4863402a3ff81c868ed24f6631df6b5a9c3fbb73e1551afae6b0dd96884","abstract_canon_sha256":"2e08088069f379eed1117fc0b387e86d989a825480725be22e9241dd954c53ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:22.882207Z","signature_b64":"xNi+7Yd+7pAsc5R0dlM2s7CdyBvekS+cu7fwh7kWlTeYwDBoAAkZhUQV2XUgpG19chqTq/7dVvmmDVGN45sSAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d2b50f5cb04c3afe6bd58a0fe28eb348701f57f7e17cf7b3a62f38a9d70d1eb","last_reissued_at":"2026-05-18T01:12:22.881721Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:22.881721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"No penalty no tears: Least squares in high-dimensional linear models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Chenlei Leng, David Dunson, Xiangyu Wang","submitted_at":"2015-06-07T05:45:24Z","abstract_excerpt":"Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size. For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step algorithms involving least squares fitting and hard thresholding. The algorithms are methodologically simple to understand intuitively, computationally easy to implement efficiently, and theoretically appealing for choosing models consistently. Numerical exercises comparing our methods with penalizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.02222","kind":"arxiv","version":5},"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":"1506.02222","created_at":"2026-05-18T01:12:22.881801+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.02222v5","created_at":"2026-05-18T01:12:22.881801+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.02222","created_at":"2026-05-18T01:12:22.881801+00:00"},{"alias_kind":"pith_short_12","alias_value":"NUVVB5OLATB2","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_16","alias_value":"NUVVB5OLATB27ZV5","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_8","alias_value":"NUVVB5OL","created_at":"2026-05-18T12:29:34.919912+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/NUVVB5OLATB27ZV5LCQP4KHLGS","json":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS.json","graph_json":"https://pith.science/api/pith-number/NUVVB5OLATB27ZV5LCQP4KHLGS/graph.json","events_json":"https://pith.science/api/pith-number/NUVVB5OLATB27ZV5LCQP4KHLGS/events.json","paper":"https://pith.science/paper/NUVVB5OL"},"agent_actions":{"view_html":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS","download_json":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS.json","view_paper":"https://pith.science/paper/NUVVB5OL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.02222&json=true","fetch_graph":"https://pith.science/api/pith-number/NUVVB5OLATB27ZV5LCQP4KHLGS/graph.json","fetch_events":"https://pith.science/api/pith-number/NUVVB5OLATB27ZV5LCQP4KHLGS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS/action/storage_attestation","attest_author":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS/action/author_attestation","sign_citation":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS/action/citation_signature","submit_replication":"https://pith.science/pith/NUVVB5OLATB27ZV5LCQP4KHLGS/action/replication_record"}},"created_at":"2026-05-18T01:12:22.881801+00:00","updated_at":"2026-05-18T01:12:22.881801+00:00"}