{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:TDENQTJK7Q234WJ4C3KVR22QVI","short_pith_number":"pith:TDENQTJK","schema_version":"1.0","canonical_sha256":"98c8d84d2afc35be593c16d558eb50aa0cf5daf21408cfaca0354e526343f976","source":{"kind":"arxiv","id":"1312.2413","version":2},"attestation_state":"computed","paper":{"title":"Likelihood analysis for a class of beta mixed models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Paulo J. Ribeiro Jr., Wagner H. Bonat, Walmes Marque Zeviani","submitted_at":"2013-12-09T12:46:18Z","abstract_excerpt":"Beta regression models are a suitable choice for continuous response variables on the unity interval. Random effects add further flexibility to the models and accommodate data structures such as hierarchical, repeated measures and longitudinal, which typically induce extra variability and/or dependence. Closed expressions cannot be obtained for parameter estimation and numerical methods are required and possibly combined with sampling algorithms. We focus on likelihood inference and related algorithms for the analysis of beta mixed models motivated by two real problems with grouped data struct"},"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":"1312.2413","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-12-09T12:46:18Z","cross_cats_sorted":[],"title_canon_sha256":"8bf7dc3eff40ba4c8ddf651a140c92fb8d76696f4a989caba4ab9641ddfbf6db","abstract_canon_sha256":"3a68d65ea3241ad397ed408ba0d54fe50050d5e1c47f76bdac7dab6121ca840d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:54.768422Z","signature_b64":"8mrSuwq3uEu0PJuD+984UUBvc7sXbv/zm6LA3tnNSYS27YHdoh017uSivlACdjDfc9EAu9fJaLFkxmOulDeWBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98c8d84d2afc35be593c16d558eb50aa0cf5daf21408cfaca0354e526343f976","last_reissued_at":"2026-05-18T00:45:54.767875Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:54.767875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Likelihood analysis for a class of beta mixed models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Paulo J. Ribeiro Jr., Wagner H. Bonat, Walmes Marque Zeviani","submitted_at":"2013-12-09T12:46:18Z","abstract_excerpt":"Beta regression models are a suitable choice for continuous response variables on the unity interval. Random effects add further flexibility to the models and accommodate data structures such as hierarchical, repeated measures and longitudinal, which typically induce extra variability and/or dependence. Closed expressions cannot be obtained for parameter estimation and numerical methods are required and possibly combined with sampling algorithms. We focus on likelihood inference and related algorithms for the analysis of beta mixed models motivated by two real problems with grouped data struct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.2413","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":"1312.2413","created_at":"2026-05-18T00:45:54.767966+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.2413v2","created_at":"2026-05-18T00:45:54.767966+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.2413","created_at":"2026-05-18T00:45:54.767966+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDENQTJK7Q23","created_at":"2026-05-18T12:28:02.375192+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDENQTJK7Q234WJ4","created_at":"2026-05-18T12:28:02.375192+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDENQTJK","created_at":"2026-05-18T12:28:02.375192+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/TDENQTJK7Q234WJ4C3KVR22QVI","json":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI.json","graph_json":"https://pith.science/api/pith-number/TDENQTJK7Q234WJ4C3KVR22QVI/graph.json","events_json":"https://pith.science/api/pith-number/TDENQTJK7Q234WJ4C3KVR22QVI/events.json","paper":"https://pith.science/paper/TDENQTJK"},"agent_actions":{"view_html":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI","download_json":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI.json","view_paper":"https://pith.science/paper/TDENQTJK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.2413&json=true","fetch_graph":"https://pith.science/api/pith-number/TDENQTJK7Q234WJ4C3KVR22QVI/graph.json","fetch_events":"https://pith.science/api/pith-number/TDENQTJK7Q234WJ4C3KVR22QVI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI/action/storage_attestation","attest_author":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI/action/author_attestation","sign_citation":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI/action/citation_signature","submit_replication":"https://pith.science/pith/TDENQTJK7Q234WJ4C3KVR22QVI/action/replication_record"}},"created_at":"2026-05-18T00:45:54.767966+00:00","updated_at":"2026-05-18T00:45:54.767966+00:00"}