{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:UKNYBC5LZRG44ZPJUDAPE3HJHI","short_pith_number":"pith:UKNYBC5L","schema_version":"1.0","canonical_sha256":"a29b808babcc4dce65e9a0c0f26ce93a1229c0eb000e89aa002c7f4b58e5b7fe","source":{"kind":"arxiv","id":"1104.1671","version":2},"attestation_state":"computed","paper":{"title":"Density-based Monte Carlo filter and its applications in estimation of unobservable variables and pharmacokinetic parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Guanghui Huang, Hui Chen, Jianping Wan","submitted_at":"2011-04-09T04:12:24Z","abstract_excerpt":"Nonlinear stochastic differential equation models with unobservable variables are now widely used in the analysis of PK/PD data. The unobservable variables are often estimated with extended Kalman filter (EKF), and the unknown pharmacokinetic parameters are usually estimated by maximum likelihood estimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE is known to be biased downwards. A density-based Monte Carlo filter (DMF) is proposed to estimate the unobservable variables, and a simulation-based procedure is proposed to estimate the unknown parameters in this paper, where a"},"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":"1104.1671","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2011-04-09T04:12:24Z","cross_cats_sorted":[],"title_canon_sha256":"5b7057df70ba0128e85d47bec639a0ce7965459bad32bfc19ec20ae00797668b","abstract_canon_sha256":"b58ed16d9f0493ce962414925388ada88b9a097e65869beb7b459e9419faaa3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:00:59.783863Z","signature_b64":"+HJfBNIWfiNn2wST9aiF44UEhV10qv4+UCdtHwiHxsWDGRqHpgYoixnfwsbGPOFWv2tAZkzBAfd6P9rrOiHnCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a29b808babcc4dce65e9a0c0f26ce93a1229c0eb000e89aa002c7f4b58e5b7fe","last_reissued_at":"2026-05-18T04:00:59.783177Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:00:59.783177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Density-based Monte Carlo filter and its applications in estimation of unobservable variables and pharmacokinetic parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Guanghui Huang, Hui Chen, Jianping Wan","submitted_at":"2011-04-09T04:12:24Z","abstract_excerpt":"Nonlinear stochastic differential equation models with unobservable variables are now widely used in the analysis of PK/PD data. The unobservable variables are often estimated with extended Kalman filter (EKF), and the unknown pharmacokinetic parameters are usually estimated by maximum likelihood estimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE is known to be biased downwards. A density-based Monte Carlo filter (DMF) is proposed to estimate the unobservable variables, and a simulation-based procedure is proposed to estimate the unknown parameters in this paper, where a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1104.1671","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":"1104.1671","created_at":"2026-05-18T04:00:59.783255+00:00"},{"alias_kind":"arxiv_version","alias_value":"1104.1671v2","created_at":"2026-05-18T04:00:59.783255+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1104.1671","created_at":"2026-05-18T04:00:59.783255+00:00"},{"alias_kind":"pith_short_12","alias_value":"UKNYBC5LZRG4","created_at":"2026-05-18T12:26:42.757692+00:00"},{"alias_kind":"pith_short_16","alias_value":"UKNYBC5LZRG44ZPJ","created_at":"2026-05-18T12:26:42.757692+00:00"},{"alias_kind":"pith_short_8","alias_value":"UKNYBC5L","created_at":"2026-05-18T12:26:42.757692+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/UKNYBC5LZRG44ZPJUDAPE3HJHI","json":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI.json","graph_json":"https://pith.science/api/pith-number/UKNYBC5LZRG44ZPJUDAPE3HJHI/graph.json","events_json":"https://pith.science/api/pith-number/UKNYBC5LZRG44ZPJUDAPE3HJHI/events.json","paper":"https://pith.science/paper/UKNYBC5L"},"agent_actions":{"view_html":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI","download_json":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI.json","view_paper":"https://pith.science/paper/UKNYBC5L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1104.1671&json=true","fetch_graph":"https://pith.science/api/pith-number/UKNYBC5LZRG44ZPJUDAPE3HJHI/graph.json","fetch_events":"https://pith.science/api/pith-number/UKNYBC5LZRG44ZPJUDAPE3HJHI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI/action/storage_attestation","attest_author":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI/action/author_attestation","sign_citation":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI/action/citation_signature","submit_replication":"https://pith.science/pith/UKNYBC5LZRG44ZPJUDAPE3HJHI/action/replication_record"}},"created_at":"2026-05-18T04:00:59.783255+00:00","updated_at":"2026-05-18T04:00:59.783255+00:00"}