{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:YIW4RP7IVVJX4KFWP4T2RMCUJ6","short_pith_number":"pith:YIW4RP7I","schema_version":"1.0","canonical_sha256":"c22dc8bfe8ad537e28b67f27a8b0544f92535ce5efb3516d43a40cd8e543a61e","source":{"kind":"arxiv","id":"1309.6058","version":1},"attestation_state":"computed","paper":{"title":"Reduced-rank Regression in Sparse Multivariate Varying-Coefficient Models with High-dimensional Covariates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Heng Lian, Shujie Ma","submitted_at":"2013-09-24T06:27:26Z","abstract_excerpt":"In genetic studies, not only can the number of predictors obtained from microarray measurements be extremely large, there can also be multiple response variables. Motivated by such a situation, we consider semiparametric dimension reduction methods in sparse multivariate regression models. Previous studies on joint variable and rank selection have focused on parametric models while here we consider the more challenging varying-coefficient models which make the investigation on nonlinear interactions of variables possible. Spline approximation, rank constraints and concave group penalties are u"},"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":"1309.6058","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-09-24T06:27:26Z","cross_cats_sorted":[],"title_canon_sha256":"5f57c6d936f07dfb3dc3450949ae21c6bdb20be262d9220d84e3272f0bb6f577","abstract_canon_sha256":"ccbb36bc9c2fb65e3411c1fbf117e5e1c75cf9203cdd85aa0b919518ac7d269f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:12:24.184197Z","signature_b64":"izLy/s8ZkEGtvaC20IPP49pGDnxDU/ZONazN6meL41o+9veM/HQ6qpKMdiR4dIMh83a4MboXZG9mkMoorD2YBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c22dc8bfe8ad537e28b67f27a8b0544f92535ce5efb3516d43a40cd8e543a61e","last_reissued_at":"2026-05-18T03:12:24.183369Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:12:24.183369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reduced-rank Regression in Sparse Multivariate Varying-Coefficient Models with High-dimensional Covariates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Heng Lian, Shujie Ma","submitted_at":"2013-09-24T06:27:26Z","abstract_excerpt":"In genetic studies, not only can the number of predictors obtained from microarray measurements be extremely large, there can also be multiple response variables. Motivated by such a situation, we consider semiparametric dimension reduction methods in sparse multivariate regression models. Previous studies on joint variable and rank selection have focused on parametric models while here we consider the more challenging varying-coefficient models which make the investigation on nonlinear interactions of variables possible. Spline approximation, rank constraints and concave group penalties are u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.6058","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":"1309.6058","created_at":"2026-05-18T03:12:24.183508+00:00"},{"alias_kind":"arxiv_version","alias_value":"1309.6058v1","created_at":"2026-05-18T03:12:24.183508+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.6058","created_at":"2026-05-18T03:12:24.183508+00:00"},{"alias_kind":"pith_short_12","alias_value":"YIW4RP7IVVJX","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_16","alias_value":"YIW4RP7IVVJX4KFW","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_8","alias_value":"YIW4RP7I","created_at":"2026-05-18T12:28:06.772260+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/YIW4RP7IVVJX4KFWP4T2RMCUJ6","json":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6.json","graph_json":"https://pith.science/api/pith-number/YIW4RP7IVVJX4KFWP4T2RMCUJ6/graph.json","events_json":"https://pith.science/api/pith-number/YIW4RP7IVVJX4KFWP4T2RMCUJ6/events.json","paper":"https://pith.science/paper/YIW4RP7I"},"agent_actions":{"view_html":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6","download_json":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6.json","view_paper":"https://pith.science/paper/YIW4RP7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1309.6058&json=true","fetch_graph":"https://pith.science/api/pith-number/YIW4RP7IVVJX4KFWP4T2RMCUJ6/graph.json","fetch_events":"https://pith.science/api/pith-number/YIW4RP7IVVJX4KFWP4T2RMCUJ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6/action/storage_attestation","attest_author":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6/action/author_attestation","sign_citation":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6/action/citation_signature","submit_replication":"https://pith.science/pith/YIW4RP7IVVJX4KFWP4T2RMCUJ6/action/replication_record"}},"created_at":"2026-05-18T03:12:24.183508+00:00","updated_at":"2026-05-18T03:12:24.183508+00:00"}