{"id":"W2008363918","doi":"10.1080/09652140020004287","title":"Multivariate modeling of missing data within and across assessment waves","year":2000,"lang":"en","type":"review","venue":"Addiction","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Missing data; Multivariate statistics; Imputation (statistics); Latent variable; Multivariate analysis; Computer science; Latent variable model; Data mining; Context (archaeology); Statistics; Longitudinal data; Econometrics; Mathematics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008090071,0.0002083751,0.0009197503,0.00004348037,0.00009292077,0.00005335905,0.0001893128,0.000177962,0.00006213906],"category_scores_gemma":[0.0004829796,0.000158962,0.00005616704,0.00009996227,0.00004891466,0.0001119326,0.0001364121,0.0002715531,0.0000017983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000315914,"about_ca_system_score_gemma":0.00009838707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005258385,"about_ca_topic_score_gemma":0.000003196432,"domain_scores_codex":[0.9983972,0.0002468882,0.0006759184,0.0003596979,0.0001690033,0.0001512405],"domain_scores_gemma":[0.9981318,0.0008984581,0.000351121,0.0005292834,0.00003474248,0.00005458603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002098112,0.00002772857,1.948179e-7,0.003753409,0.00007286714,0.000002420269,0.00009884011,0.000001072024,3.916392e-7,0.007042881,0.00002645635,0.9889717],"study_design_scores_gemma":[0.0003528357,0.00008628202,0.00000497769,0.04213687,0.001860481,0.00008736278,0.00009134801,0.5508634,0.000001466684,0.3159983,0.08788043,0.0006361839],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.000001449168,0.3740987,0.6247534,0.000002624627,0.00008718045,0.0001800142,0.0005298035,0.00002235166,0.0003244742],"genre_scores_gemma":[0.00000994825,0.5072346,0.4925765,0.00000180205,0.00004535267,0.000006637903,0.00009628194,0.00001850558,0.00001037402],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9883354,"threshold_uncertainty_score":0.6482281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2957922079733491,"score_gpt":0.5143796784453989,"score_spread":0.2185874704720498,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}