{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZCD4UFLU6LCAAGSRTSMC7FAANB","short_pith_number":"pith:ZCD4UFLU","schema_version":"1.0","canonical_sha256":"c887ca1574f2c4001a519c982f94006840bd3fdb425b13b60c38052a12ad082e","source":{"kind":"arxiv","id":"2411.15822","version":2},"attestation_state":"computed","paper":{"title":"Semi-parametric least-area linear-circular regression through M\\\"{o}bius transformation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Buddhananda Banerjee, Surojit Biswas","submitted_at":"2024-11-24T12:53:09Z","abstract_excerpt":"This paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized M\\\"{o}bius transformation to define the regression curve. By mapping the real axis to the circle, the model effectively captures the relationship between linear and angular components. A key innovation is the introduction of an area-based loss function, inspired by the geometry of a curved torus, for efficient parameter estimation. The semi-parametric nature of the model eliminates the need for specific distributional assumptions about the angular error, enhanc"},"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":"2411.15822","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-11-24T12:53:09Z","cross_cats_sorted":[],"title_canon_sha256":"d2f76b7432ed096e7947ea68e0a265001bd205ecf0c7c7f3ebbe522c540d964b","abstract_canon_sha256":"a14d910fc83bace326b22636ab00e243744a2c5af1f17257dc85c107b858c3bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:58.553860Z","signature_b64":"1koYCivc/luvTlZo+2dwQW8I1Sk9cwRb5Tb2f6VORvyzRV7TxDz4eH+n77936z4bypMSRtZrRt39qZ4nA9vqBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c887ca1574f2c4001a519c982f94006840bd3fdb425b13b60c38052a12ad082e","last_reissued_at":"2026-07-05T09:45:58.553312Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:58.553312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semi-parametric least-area linear-circular regression through M\\\"{o}bius transformation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Buddhananda Banerjee, Surojit Biswas","submitted_at":"2024-11-24T12:53:09Z","abstract_excerpt":"This paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized M\\\"{o}bius transformation to define the regression curve. By mapping the real axis to the circle, the model effectively captures the relationship between linear and angular components. A key innovation is the introduction of an area-based loss function, inspired by the geometry of a curved torus, for efficient parameter estimation. The semi-parametric nature of the model eliminates the need for specific distributional assumptions about the angular error, enhanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15822","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.15822/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2411.15822","created_at":"2026-07-05T09:45:58.553404+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15822v2","created_at":"2026-07-05T09:45:58.553404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15822","created_at":"2026-07-05T09:45:58.553404+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZCD4UFLU6LCA","created_at":"2026-07-05T09:45:58.553404+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZCD4UFLU6LCAAGSR","created_at":"2026-07-05T09:45:58.553404+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZCD4UFLU","created_at":"2026-07-05T09:45:58.553404+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/ZCD4UFLU6LCAAGSRTSMC7FAANB","json":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB.json","graph_json":"https://pith.science/api/pith-number/ZCD4UFLU6LCAAGSRTSMC7FAANB/graph.json","events_json":"https://pith.science/api/pith-number/ZCD4UFLU6LCAAGSRTSMC7FAANB/events.json","paper":"https://pith.science/paper/ZCD4UFLU"},"agent_actions":{"view_html":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB","download_json":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB.json","view_paper":"https://pith.science/paper/ZCD4UFLU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15822&json=true","fetch_graph":"https://pith.science/api/pith-number/ZCD4UFLU6LCAAGSRTSMC7FAANB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZCD4UFLU6LCAAGSRTSMC7FAANB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB/action/storage_attestation","attest_author":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB/action/author_attestation","sign_citation":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB/action/citation_signature","submit_replication":"https://pith.science/pith/ZCD4UFLU6LCAAGSRTSMC7FAANB/action/replication_record"}},"created_at":"2026-07-05T09:45:58.553404+00:00","updated_at":"2026-07-05T09:45:58.553404+00:00"}