{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2UWMV4HPOVY52HLYC6PL4GVFBK","short_pith_number":"pith:2UWMV4HP","schema_version":"1.0","canonical_sha256":"d52ccaf0ef7571dd1d78179ebe1aa50a854acbee463300d29513a4c7c1a571f9","source":{"kind":"arxiv","id":"1904.04664","version":1},"attestation_state":"computed","paper":{"title":"Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Hu Yang, Siwei Xia, Yuehan Yang","submitted_at":"2019-04-09T13:41:20Z","abstract_excerpt":"This paper considers a high-dimensional linear regression problem where there are complex correlation structures among predictors. We propose a graph-constrained regularization procedure, named Sparse Laplacian Shrinkage with the Graphical Lasso Estimator (SLS-GLE). The procedure uses the estimated precision matrix to describe the specific information on the conditional dependence pattern among predictors, and encourages both sparsity on the regression model and the graphical model. We introduce the Laplacian quadratic penalty adopting the graph information, and give detailed discussions on th"},"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":"1904.04664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-04-09T13:41:20Z","cross_cats_sorted":[],"title_canon_sha256":"378a92e4bb32a7970588af82bda7d4f1f40a820e6f3507d50c051f62ab48c7a4","abstract_canon_sha256":"b7d83d4fc4e5da2cf598c47839af811449aee96d0be6108305e34f2659536852"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:58.484675Z","signature_b64":"VK5zri9Yay0ngGVtjwrbgD+KqYMvtnfppA86LHyoa5mBHwLTDfMYMf9AnSM0Zvpg7ZKYUsWk1QkBWmSVtJSoAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d52ccaf0ef7571dd1d78179ebe1aa50a854acbee463300d29513a4c7c1a571f9","last_reissued_at":"2026-05-17T23:48:58.484266Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:58.484266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Hu Yang, Siwei Xia, Yuehan Yang","submitted_at":"2019-04-09T13:41:20Z","abstract_excerpt":"This paper considers a high-dimensional linear regression problem where there are complex correlation structures among predictors. We propose a graph-constrained regularization procedure, named Sparse Laplacian Shrinkage with the Graphical Lasso Estimator (SLS-GLE). The procedure uses the estimated precision matrix to describe the specific information on the conditional dependence pattern among predictors, and encourages both sparsity on the regression model and the graphical model. We introduce the Laplacian quadratic penalty adopting the graph information, and give detailed discussions on th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04664","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":"1904.04664","created_at":"2026-05-17T23:48:58.484347+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.04664v1","created_at":"2026-05-17T23:48:58.484347+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04664","created_at":"2026-05-17T23:48:58.484347+00:00"},{"alias_kind":"pith_short_12","alias_value":"2UWMV4HPOVY5","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"2UWMV4HPOVY52HLY","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"2UWMV4HP","created_at":"2026-05-18T12:33:07.085635+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/2UWMV4HPOVY52HLYC6PL4GVFBK","json":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK.json","graph_json":"https://pith.science/api/pith-number/2UWMV4HPOVY52HLYC6PL4GVFBK/graph.json","events_json":"https://pith.science/api/pith-number/2UWMV4HPOVY52HLYC6PL4GVFBK/events.json","paper":"https://pith.science/paper/2UWMV4HP"},"agent_actions":{"view_html":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK","download_json":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK.json","view_paper":"https://pith.science/paper/2UWMV4HP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.04664&json=true","fetch_graph":"https://pith.science/api/pith-number/2UWMV4HPOVY52HLYC6PL4GVFBK/graph.json","fetch_events":"https://pith.science/api/pith-number/2UWMV4HPOVY52HLYC6PL4GVFBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK/action/storage_attestation","attest_author":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK/action/author_attestation","sign_citation":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK/action/citation_signature","submit_replication":"https://pith.science/pith/2UWMV4HPOVY52HLYC6PL4GVFBK/action/replication_record"}},"created_at":"2026-05-17T23:48:58.484347+00:00","updated_at":"2026-05-17T23:48:58.484347+00:00"}