{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6VUBC6JI3BHRCXOMWO7VF7XEWA","short_pith_number":"pith:6VUBC6JI","schema_version":"1.0","canonical_sha256":"f568117928d84f115dccb3bf52fee4b037ca7f9240a3637a5da4f99cebe4a7e7","source":{"kind":"arxiv","id":"1707.02182","version":1},"attestation_state":"computed","paper":{"title":"A bi-dimensional finite mixture model for longitudinal data subject to dropout","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Alessandra Spagnoli, Marco Alf\\`o, Maria Francesca Marino","submitted_at":"2017-07-07T13:59:47Z","abstract_excerpt":"In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, conditional on the available covariates and the observed responses, still depends on unobserved outcomes, the dropout mechanism is said to be non ignorable. A common objective is to build a reliable association structure to account for dependence between the longitudinal and the dropout processes. Starting from the existing literature, we introduce a random coefficient based dropout model where the association between ou"},"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":"1707.02182","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-07-07T13:59:47Z","cross_cats_sorted":[],"title_canon_sha256":"ba06e68f0d29d1a837873b79d651a6627a0c6296e6f0546b16fd4b50c7d22234","abstract_canon_sha256":"1324e92c91189b53f079c22954f80f6b861e21b6503a0767921354f62e949361"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:42.625880Z","signature_b64":"YJMLbyqRc3/Em9XUsI2nVQLpqM3sT31+nTz0UcIUzaDV9w5xnurVXifDiDWKY0mP2XN0aa7m1osLblloBfg1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f568117928d84f115dccb3bf52fee4b037ca7f9240a3637a5da4f99cebe4a7e7","last_reissued_at":"2026-05-18T00:40:42.625204Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:42.625204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A bi-dimensional finite mixture model for longitudinal data subject to dropout","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Alessandra Spagnoli, Marco Alf\\`o, Maria Francesca Marino","submitted_at":"2017-07-07T13:59:47Z","abstract_excerpt":"In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, conditional on the available covariates and the observed responses, still depends on unobserved outcomes, the dropout mechanism is said to be non ignorable. A common objective is to build a reliable association structure to account for dependence between the longitudinal and the dropout processes. Starting from the existing literature, we introduce a random coefficient based dropout model where the association between ou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.02182","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":"1707.02182","created_at":"2026-05-18T00:40:42.625293+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.02182v1","created_at":"2026-05-18T00:40:42.625293+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.02182","created_at":"2026-05-18T00:40:42.625293+00:00"},{"alias_kind":"pith_short_12","alias_value":"6VUBC6JI3BHR","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6VUBC6JI3BHRCXOM","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6VUBC6JI","created_at":"2026-05-18T12:31:03.183658+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/6VUBC6JI3BHRCXOMWO7VF7XEWA","json":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA.json","graph_json":"https://pith.science/api/pith-number/6VUBC6JI3BHRCXOMWO7VF7XEWA/graph.json","events_json":"https://pith.science/api/pith-number/6VUBC6JI3BHRCXOMWO7VF7XEWA/events.json","paper":"https://pith.science/paper/6VUBC6JI"},"agent_actions":{"view_html":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA","download_json":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA.json","view_paper":"https://pith.science/paper/6VUBC6JI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.02182&json=true","fetch_graph":"https://pith.science/api/pith-number/6VUBC6JI3BHRCXOMWO7VF7XEWA/graph.json","fetch_events":"https://pith.science/api/pith-number/6VUBC6JI3BHRCXOMWO7VF7XEWA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA/action/storage_attestation","attest_author":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA/action/author_attestation","sign_citation":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA/action/citation_signature","submit_replication":"https://pith.science/pith/6VUBC6JI3BHRCXOMWO7VF7XEWA/action/replication_record"}},"created_at":"2026-05-18T00:40:42.625293+00:00","updated_at":"2026-05-18T00:40:42.625293+00:00"}