{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:MCTE5OFC53T6OBRT5Y7MKC2CPL","short_pith_number":"pith:MCTE5OFC","schema_version":"1.0","canonical_sha256":"60a64eb8a2eee7e70633ee3ec50b427ad4481828cd0490703760bed515082c2f","source":{"kind":"arxiv","id":"1403.7644","version":1},"attestation_state":"computed","paper":{"title":"Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.AP","authors_text":"Andrew T. Karl, Sharon L. Lohr, Yan Yang","submitted_at":"2014-03-29T16:22:59Z","abstract_excerpt":"The generalized persistence (GP) model, developed in the context of estimating ``value added'' by individual teachers to their students' current and future test scores, is one of the most flexible value-added models in the literature. Although developed in the educational setting, the GP model can potentially be applied to any structure where each sequential response of a lower-level unit may be associated with a different higher-level unit, and the effects of the higher-level units may persist over time. The flexibility of the GP model, however, and its multiple membership random effects stru"},"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":"1403.7644","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2014-03-29T16:22:59Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"d034fd87c3264a0fcfd3e75d46457f8b43c56a757c87187e5e41e39ca19c8e82","abstract_canon_sha256":"506a124a74dad5b187b9d85b311876876756cfe6d0ed2b7e2b091c4476f6dc0d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:55:12.798475Z","signature_b64":"2DKquRuLGPnFbr2hOAhDaymFP/TGPSi0Tck673OmDHsha/u2eelmkdA6Gg9xnYWkVmOoHZAF3r8hcAdj0HUDAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60a64eb8a2eee7e70633ee3ec50b427ad4481828cd0490703760bed515082c2f","last_reissued_at":"2026-05-18T02:55:12.797939Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:55:12.797939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.AP","authors_text":"Andrew T. Karl, Sharon L. Lohr, Yan Yang","submitted_at":"2014-03-29T16:22:59Z","abstract_excerpt":"The generalized persistence (GP) model, developed in the context of estimating ``value added'' by individual teachers to their students' current and future test scores, is one of the most flexible value-added models in the literature. Although developed in the educational setting, the GP model can potentially be applied to any structure where each sequential response of a lower-level unit may be associated with a different higher-level unit, and the effects of the higher-level units may persist over time. The flexibility of the GP model, however, and its multiple membership random effects stru"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.7644","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":"1403.7644","created_at":"2026-05-18T02:55:12.798013+00:00"},{"alias_kind":"arxiv_version","alias_value":"1403.7644v1","created_at":"2026-05-18T02:55:12.798013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.7644","created_at":"2026-05-18T02:55:12.798013+00:00"},{"alias_kind":"pith_short_12","alias_value":"MCTE5OFC53T6","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_16","alias_value":"MCTE5OFC53T6OBRT","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_8","alias_value":"MCTE5OFC","created_at":"2026-05-18T12:28:38.356838+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/MCTE5OFC53T6OBRT5Y7MKC2CPL","json":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL.json","graph_json":"https://pith.science/api/pith-number/MCTE5OFC53T6OBRT5Y7MKC2CPL/graph.json","events_json":"https://pith.science/api/pith-number/MCTE5OFC53T6OBRT5Y7MKC2CPL/events.json","paper":"https://pith.science/paper/MCTE5OFC"},"agent_actions":{"view_html":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL","download_json":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL.json","view_paper":"https://pith.science/paper/MCTE5OFC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1403.7644&json=true","fetch_graph":"https://pith.science/api/pith-number/MCTE5OFC53T6OBRT5Y7MKC2CPL/graph.json","fetch_events":"https://pith.science/api/pith-number/MCTE5OFC53T6OBRT5Y7MKC2CPL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL/action/storage_attestation","attest_author":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL/action/author_attestation","sign_citation":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL/action/citation_signature","submit_replication":"https://pith.science/pith/MCTE5OFC53T6OBRT5Y7MKC2CPL/action/replication_record"}},"created_at":"2026-05-18T02:55:12.798013+00:00","updated_at":"2026-05-18T02:55:12.798013+00:00"}