{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:HGGSXH4L5B25ERNDX6ZO7JIPGU","short_pith_number":"pith:HGGSXH4L","schema_version":"1.0","canonical_sha256":"398d2b9f8be875d245a3bfb2efa50f3507077e51bea40e985f198a80bbc75a03","source":{"kind":"arxiv","id":"1408.0318","version":1},"attestation_state":"computed","paper":{"title":"Jointly Sparse Global SIMPLS Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Alfred O. Hero, Arthur Tenenhaus, Dennis Wei, Laura Trinchera, Tzu-Yu Liu","submitted_at":"2014-08-01T23:19:49Z","abstract_excerpt":"Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. Since partial least squares regression (PLS-R) does not require matrix inversion or diagonalization, it can be applied to problems with large numbers of variables. As predictor dimension increases, variable selection becomes essential to avoid over-fitting, to provide more accurate predictors and to yield more interpretable parameters. We propose a global variable selection approach that penalizes the total number of variables across all PLS components. Put another way, the pr"},"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":"1408.0318","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-08-01T23:19:49Z","cross_cats_sorted":[],"title_canon_sha256":"42fb52ad177746a6d439f33dd68ee36a0a212132b84b012eeb432b540f38d865","abstract_canon_sha256":"8c4d312b10f99a26584038e0e7e30f84b117fc6fb4ced1deaff8115f64261de5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:45:57.377200Z","signature_b64":"N9tFGEW1TDfWGOc9vS+BMP39NmqWPN3GYwNZhTEjgtE695XPLIW2Skf0QXtCtW9P5/VNXnCjIJ2atkfI+C+IBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"398d2b9f8be875d245a3bfb2efa50f3507077e51bea40e985f198a80bbc75a03","last_reissued_at":"2026-05-18T02:45:57.376699Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:45:57.376699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Jointly Sparse Global SIMPLS Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Alfred O. Hero, Arthur Tenenhaus, Dennis Wei, Laura Trinchera, Tzu-Yu Liu","submitted_at":"2014-08-01T23:19:49Z","abstract_excerpt":"Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. Since partial least squares regression (PLS-R) does not require matrix inversion or diagonalization, it can be applied to problems with large numbers of variables. As predictor dimension increases, variable selection becomes essential to avoid over-fitting, to provide more accurate predictors and to yield more interpretable parameters. We propose a global variable selection approach that penalizes the total number of variables across all PLS components. Put another way, the pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.0318","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":"1408.0318","created_at":"2026-05-18T02:45:57.376763+00:00"},{"alias_kind":"arxiv_version","alias_value":"1408.0318v1","created_at":"2026-05-18T02:45:57.376763+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.0318","created_at":"2026-05-18T02:45:57.376763+00:00"},{"alias_kind":"pith_short_12","alias_value":"HGGSXH4L5B25","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_16","alias_value":"HGGSXH4L5B25ERND","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_8","alias_value":"HGGSXH4L","created_at":"2026-05-18T12:28:30.664211+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/HGGSXH4L5B25ERNDX6ZO7JIPGU","json":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU.json","graph_json":"https://pith.science/api/pith-number/HGGSXH4L5B25ERNDX6ZO7JIPGU/graph.json","events_json":"https://pith.science/api/pith-number/HGGSXH4L5B25ERNDX6ZO7JIPGU/events.json","paper":"https://pith.science/paper/HGGSXH4L"},"agent_actions":{"view_html":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU","download_json":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU.json","view_paper":"https://pith.science/paper/HGGSXH4L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1408.0318&json=true","fetch_graph":"https://pith.science/api/pith-number/HGGSXH4L5B25ERNDX6ZO7JIPGU/graph.json","fetch_events":"https://pith.science/api/pith-number/HGGSXH4L5B25ERNDX6ZO7JIPGU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU/action/storage_attestation","attest_author":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU/action/author_attestation","sign_citation":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU/action/citation_signature","submit_replication":"https://pith.science/pith/HGGSXH4L5B25ERNDX6ZO7JIPGU/action/replication_record"}},"created_at":"2026-05-18T02:45:57.376763+00:00","updated_at":"2026-05-18T02:45:57.376763+00:00"}