{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:HZ3X533ZUC4UUNQYNCLWLR3K5Y","short_pith_number":"pith:HZ3X533Z","schema_version":"1.0","canonical_sha256":"3e777eef79a0b94a3618689765c76aee10bc8ebda978e1feb7ab3a36d5b286e1","source":{"kind":"arxiv","id":"1409.0391","version":2},"attestation_state":"computed","paper":{"title":"Estimating Linear Mixed-effects State Space Model Based on Disturbance Smoothing","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Aiping Tang, Jie Zhou","submitted_at":"2014-09-01T12:50:47Z","abstract_excerpt":"We extend the linear mixed-effects state model to accommodate the correlated individuals and investigate its parameter and state estimation based on disturbance smoothing in this paper. For parameter estimation, EM and score based algorithms are considered. Intermediate quantity of EM algorithm is investigated firstly from which the explicit recursive formulas for the maximizer of the intermediate quantity are derived out for two given models. As for score based algorithms, explicit formulas for the score vector are achieved from which it is shown that the maximum likelihood estimation is equi"},"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":"1409.0391","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2014-09-01T12:50:47Z","cross_cats_sorted":[],"title_canon_sha256":"a74218e2822e052c9f5403dc8264074a147d2388638ab531d1d2bee5c0e148f6","abstract_canon_sha256":"c97e038ab464aee0aab7fd96a9e5b2fe6867ad65649dd2ffe837495a0fd231db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:46.624084Z","signature_b64":"RJSAlqsl8SBCuqSC9p35s079U39Up4tgtwYV8WwiFdMmj405QKbZf6LjkIK66A9dbn7VCv/vE3oXP4YOmmh4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e777eef79a0b94a3618689765c76aee10bc8ebda978e1feb7ab3a36d5b286e1","last_reissued_at":"2026-05-18T02:43:46.623663Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:46.623663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Linear Mixed-effects State Space Model Based on Disturbance Smoothing","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Aiping Tang, Jie Zhou","submitted_at":"2014-09-01T12:50:47Z","abstract_excerpt":"We extend the linear mixed-effects state model to accommodate the correlated individuals and investigate its parameter and state estimation based on disturbance smoothing in this paper. For parameter estimation, EM and score based algorithms are considered. Intermediate quantity of EM algorithm is investigated firstly from which the explicit recursive formulas for the maximizer of the intermediate quantity are derived out for two given models. As for score based algorithms, explicit formulas for the score vector are achieved from which it is shown that the maximum likelihood estimation is equi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.0391","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":"1409.0391","created_at":"2026-05-18T02:43:46.623722+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.0391v2","created_at":"2026-05-18T02:43:46.623722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.0391","created_at":"2026-05-18T02:43:46.623722+00:00"},{"alias_kind":"pith_short_12","alias_value":"HZ3X533ZUC4U","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"HZ3X533ZUC4UUNQY","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"HZ3X533Z","created_at":"2026-05-18T12:28:33.132498+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/HZ3X533ZUC4UUNQYNCLWLR3K5Y","json":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y.json","graph_json":"https://pith.science/api/pith-number/HZ3X533ZUC4UUNQYNCLWLR3K5Y/graph.json","events_json":"https://pith.science/api/pith-number/HZ3X533ZUC4UUNQYNCLWLR3K5Y/events.json","paper":"https://pith.science/paper/HZ3X533Z"},"agent_actions":{"view_html":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y","download_json":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y.json","view_paper":"https://pith.science/paper/HZ3X533Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.0391&json=true","fetch_graph":"https://pith.science/api/pith-number/HZ3X533ZUC4UUNQYNCLWLR3K5Y/graph.json","fetch_events":"https://pith.science/api/pith-number/HZ3X533ZUC4UUNQYNCLWLR3K5Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y/action/storage_attestation","attest_author":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y/action/author_attestation","sign_citation":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y/action/citation_signature","submit_replication":"https://pith.science/pith/HZ3X533ZUC4UUNQYNCLWLR3K5Y/action/replication_record"}},"created_at":"2026-05-18T02:43:46.623722+00:00","updated_at":"2026-05-18T02:43:46.623722+00:00"}