{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:7P3FYECF5OMMCWHEDX6IRA3VXI","short_pith_number":"pith:7P3FYECF","schema_version":"1.0","canonical_sha256":"fbf65c1045eb98c158e41dfc888375ba33c79b9251639e0f307614d3dfc82944","source":{"kind":"arxiv","id":"1110.3689","version":2},"attestation_state":"computed","paper":{"title":"Efficient Bayesian Multivariate Surface Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.CO","authors_text":"Feng Li, Mattias Villani","submitted_at":"2011-10-17T15:00:42Z","abstract_excerpt":"Methods for choosing a fixed set of knot locations in additive spline models are fairly well established in the statistical literature. While most of these methods are in principle directly extendable to non-additive surface models, they are less likely to be successful in that setting because of the curse of dimensionality, especially when there are more than a couple of covariates. We propose a regression model for a multivariate Gaussian response that combines both additive splines and interactive splines, and a highly efficient MCMC algorithm that updates all the knot locations jointly. We"},"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":"1110.3689","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2011-10-17T15:00:42Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"b304d5d06c4df42ff1c965daf570f1e65cb982fb6058b5f8e27095b5c2ae8e9d","abstract_canon_sha256":"8537675715707dc204e55569337825f2def3eba8f3b8faed34b93971ff4fa44e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:01.407186Z","signature_b64":"AbJ6dRVNcn0MtmyslowAPe6g2eP8eBFPn56Tdh33B2MfHwGVn6+z8Qk8VRbYbJ19K051GGTCO6nAzmx64EzlDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbf65c1045eb98c158e41dfc888375ba33c79b9251639e0f307614d3dfc82944","last_reissued_at":"2026-05-18T00:12:01.406481Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:01.406481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Bayesian Multivariate Surface Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.CO","authors_text":"Feng Li, Mattias Villani","submitted_at":"2011-10-17T15:00:42Z","abstract_excerpt":"Methods for choosing a fixed set of knot locations in additive spline models are fairly well established in the statistical literature. While most of these methods are in principle directly extendable to non-additive surface models, they are less likely to be successful in that setting because of the curse of dimensionality, especially when there are more than a couple of covariates. We propose a regression model for a multivariate Gaussian response that combines both additive splines and interactive splines, and a highly efficient MCMC algorithm that updates all the knot locations jointly. We"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1110.3689","kind":"arxiv","version":2},"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":"1110.3689","created_at":"2026-05-18T00:12:01.406584+00:00"},{"alias_kind":"arxiv_version","alias_value":"1110.3689v2","created_at":"2026-05-18T00:12:01.406584+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1110.3689","created_at":"2026-05-18T00:12:01.406584+00:00"},{"alias_kind":"pith_short_12","alias_value":"7P3FYECF5OMM","created_at":"2026-05-18T12:26:22.705136+00:00"},{"alias_kind":"pith_short_16","alias_value":"7P3FYECF5OMMCWHE","created_at":"2026-05-18T12:26:22.705136+00:00"},{"alias_kind":"pith_short_8","alias_value":"7P3FYECF","created_at":"2026-05-18T12:26:22.705136+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/7P3FYECF5OMMCWHEDX6IRA3VXI","json":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI.json","graph_json":"https://pith.science/api/pith-number/7P3FYECF5OMMCWHEDX6IRA3VXI/graph.json","events_json":"https://pith.science/api/pith-number/7P3FYECF5OMMCWHEDX6IRA3VXI/events.json","paper":"https://pith.science/paper/7P3FYECF"},"agent_actions":{"view_html":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI","download_json":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI.json","view_paper":"https://pith.science/paper/7P3FYECF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1110.3689&json=true","fetch_graph":"https://pith.science/api/pith-number/7P3FYECF5OMMCWHEDX6IRA3VXI/graph.json","fetch_events":"https://pith.science/api/pith-number/7P3FYECF5OMMCWHEDX6IRA3VXI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI/action/storage_attestation","attest_author":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI/action/author_attestation","sign_citation":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI/action/citation_signature","submit_replication":"https://pith.science/pith/7P3FYECF5OMMCWHEDX6IRA3VXI/action/replication_record"}},"created_at":"2026-05-18T00:12:01.406584+00:00","updated_at":"2026-05-18T00:12:01.406584+00:00"}