{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:RZU7LTGKE2APE3VPRYBM4FRBBH","short_pith_number":"pith:RZU7LTGK","schema_version":"1.0","canonical_sha256":"8e69f5ccca2680f26eaf8e02ce162109f2356a54da8fdc9941cc02c7bf6982de","source":{"kind":"arxiv","id":"1211.3930","version":2},"attestation_state":"computed","paper":{"title":"A Geometrical Approach to Iterative Isotone Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander B. N\\'emeth, Arnaud Guyader, Nicolas J\\'egou, S\\'andor Z. N\\'emeth","submitted_at":"2012-11-16T15:51:25Z","abstract_excerpt":"In the present paper, we propose and analyze a novel method for estimating a univariate regression function of bounded variation. The underpinning idea is to combine two classical tools in nonparametric statistics, namely isotonic regression and the estimation of additive models. A geometrical interpretation enables us to link this iterative method with Von Neumann's algorithm. Moreover, making a connection with the general property of isotonicity of projection onto convex cones, we derive another equivalent algorithm and go further in the analysis. As iterating the algorithm leads to overfitt"},"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":"1211.3930","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2012-11-16T15:51:25Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"c8ffd9bb176bdac0c6e10263930fc933353bbafab6a39952a4de9fafe2b82b4e","abstract_canon_sha256":"d58f57a6b936650d1e2147d391d52105fc37e4e434ecd9d599e3173e77a3b778"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:40:28.228189Z","signature_b64":"o+n9s0E0PHGe9a8BngyHVmCYLCFXLiyBALEDJ3Eq1ahmJ8K/l08ppSX5f8ouPNUcHV17AE2qNN98FhwpRYzcAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e69f5ccca2680f26eaf8e02ce162109f2356a54da8fdc9941cc02c7bf6982de","last_reissued_at":"2026-05-18T03:40:28.227520Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:40:28.227520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Geometrical Approach to Iterative Isotone Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander B. N\\'emeth, Arnaud Guyader, Nicolas J\\'egou, S\\'andor Z. N\\'emeth","submitted_at":"2012-11-16T15:51:25Z","abstract_excerpt":"In the present paper, we propose and analyze a novel method for estimating a univariate regression function of bounded variation. The underpinning idea is to combine two classical tools in nonparametric statistics, namely isotonic regression and the estimation of additive models. A geometrical interpretation enables us to link this iterative method with Von Neumann's algorithm. Moreover, making a connection with the general property of isotonicity of projection onto convex cones, we derive another equivalent algorithm and go further in the analysis. As iterating the algorithm leads to overfitt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.3930","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":"1211.3930","created_at":"2026-05-18T03:40:28.227613+00:00"},{"alias_kind":"arxiv_version","alias_value":"1211.3930v2","created_at":"2026-05-18T03:40:28.227613+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.3930","created_at":"2026-05-18T03:40:28.227613+00:00"},{"alias_kind":"pith_short_12","alias_value":"RZU7LTGKE2AP","created_at":"2026-05-18T12:27:20.899486+00:00"},{"alias_kind":"pith_short_16","alias_value":"RZU7LTGKE2APE3VP","created_at":"2026-05-18T12:27:20.899486+00:00"},{"alias_kind":"pith_short_8","alias_value":"RZU7LTGK","created_at":"2026-05-18T12:27:20.899486+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/RZU7LTGKE2APE3VPRYBM4FRBBH","json":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH.json","graph_json":"https://pith.science/api/pith-number/RZU7LTGKE2APE3VPRYBM4FRBBH/graph.json","events_json":"https://pith.science/api/pith-number/RZU7LTGKE2APE3VPRYBM4FRBBH/events.json","paper":"https://pith.science/paper/RZU7LTGK"},"agent_actions":{"view_html":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH","download_json":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH.json","view_paper":"https://pith.science/paper/RZU7LTGK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1211.3930&json=true","fetch_graph":"https://pith.science/api/pith-number/RZU7LTGKE2APE3VPRYBM4FRBBH/graph.json","fetch_events":"https://pith.science/api/pith-number/RZU7LTGKE2APE3VPRYBM4FRBBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH/action/storage_attestation","attest_author":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH/action/author_attestation","sign_citation":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH/action/citation_signature","submit_replication":"https://pith.science/pith/RZU7LTGKE2APE3VPRYBM4FRBBH/action/replication_record"}},"created_at":"2026-05-18T03:40:28.227613+00:00","updated_at":"2026-05-18T03:40:28.227613+00:00"}