{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:LLQ46DEEWIKGV3UWBTNMBPEOUW","short_pith_number":"pith:LLQ46DEE","schema_version":"1.0","canonical_sha256":"5ae1cf0c84b2146aee960cdac0bc8ea5b416f108b634309521beb5c4463053e8","source":{"kind":"arxiv","id":"1312.6978","version":1},"attestation_state":"computed","paper":{"title":"Mod\\`ele \\`a processus latent et algorithme EM pour la r\\'egression non lin\\'eaire","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Allou Sam\\'e, Faicel Chamroukhi, G\\'erard Govaert, Patrice Aknin","submitted_at":"2013-12-25T14:21:48Z","abstract_excerpt":"A non linear regression approach which consists of a specific regression model incorporating a latent process, allowing various polynomial regression models to be activated preferentially and smoothly, is introduced in this paper. The model parameters are estimated by maximum likelihood performed via a dedicated expecation-maximization (EM) algorithm. An experimental study using simulated and real data sets reveals good performances of the proposed approach."},"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":"1312.6978","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-12-25T14:21:48Z","cross_cats_sorted":["cs.LG","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"a762bf1dbf14e486a88ad9a2fb58cce2618cf73d65ed2718e46e4972f71f2af0","abstract_canon_sha256":"dbe35d7aa9bb97f55b0e77bd6dbbf659c0c3122a224c828cce91841e428045f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:03:46.243250Z","signature_b64":"Z/WVKdeay9onZyTqKU2K1TiRZRv29TZrnoBNiuv0/RYElMDBq/mRujl53LycNK+Gug0tC5zLh/1smQYYn20SDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ae1cf0c84b2146aee960cdac0bc8ea5b416f108b634309521beb5c4463053e8","last_reissued_at":"2026-05-18T03:03:46.242707Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:03:46.242707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mod\\`ele \\`a processus latent et algorithme EM pour la r\\'egression non lin\\'eaire","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Allou Sam\\'e, Faicel Chamroukhi, G\\'erard Govaert, Patrice Aknin","submitted_at":"2013-12-25T14:21:48Z","abstract_excerpt":"A non linear regression approach which consists of a specific regression model incorporating a latent process, allowing various polynomial regression models to be activated preferentially and smoothly, is introduced in this paper. The model parameters are estimated by maximum likelihood performed via a dedicated expecation-maximization (EM) algorithm. An experimental study using simulated and real data sets reveals good performances of the proposed approach."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.6978","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":"1312.6978","created_at":"2026-05-18T03:03:46.242800+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.6978v1","created_at":"2026-05-18T03:03:46.242800+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.6978","created_at":"2026-05-18T03:03:46.242800+00:00"},{"alias_kind":"pith_short_12","alias_value":"LLQ46DEEWIKG","created_at":"2026-05-18T12:27:51.066281+00:00"},{"alias_kind":"pith_short_16","alias_value":"LLQ46DEEWIKGV3UW","created_at":"2026-05-18T12:27:51.066281+00:00"},{"alias_kind":"pith_short_8","alias_value":"LLQ46DEE","created_at":"2026-05-18T12:27:51.066281+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/LLQ46DEEWIKGV3UWBTNMBPEOUW","json":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW.json","graph_json":"https://pith.science/api/pith-number/LLQ46DEEWIKGV3UWBTNMBPEOUW/graph.json","events_json":"https://pith.science/api/pith-number/LLQ46DEEWIKGV3UWBTNMBPEOUW/events.json","paper":"https://pith.science/paper/LLQ46DEE"},"agent_actions":{"view_html":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW","download_json":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW.json","view_paper":"https://pith.science/paper/LLQ46DEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.6978&json=true","fetch_graph":"https://pith.science/api/pith-number/LLQ46DEEWIKGV3UWBTNMBPEOUW/graph.json","fetch_events":"https://pith.science/api/pith-number/LLQ46DEEWIKGV3UWBTNMBPEOUW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW/action/storage_attestation","attest_author":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW/action/author_attestation","sign_citation":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW/action/citation_signature","submit_replication":"https://pith.science/pith/LLQ46DEEWIKGV3UWBTNMBPEOUW/action/replication_record"}},"created_at":"2026-05-18T03:03:46.242800+00:00","updated_at":"2026-05-18T03:03:46.242800+00:00"}