{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02572404,0.001476565,0.003856212,0.004432844,0.000716794,0.002616286,0.006143097,0.002885392,0.003944779],"category_scores_gemma":[0.06381491,0.0008588543,0.002316277,0.008515859,0.002451648,0.004312353,0.00181293,0.003484812,0.002803791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001775383,"about_ca_system_score_gemma":0.004009129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003089939,"about_ca_topic_score_gemma":0.004042308,"domain_scores_codex":[0.9836048,0.01216736,0.0006536862,0.001086822,0.002331867,0.0001554011],"domain_scores_gemma":[0.9269527,0.06322295,0.003174888,0.003352944,0.003011795,0.0002846678],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006799644,0.0000801862,0.003959159,0.005077321,0.0007199174,0.0002428516,0.0004456742,0.0138764,0.000241665,0.1359222,0.0155471,0.8238195],"study_design_scores_gemma":[0.00006897215,0.0001952243,0.008300507,0.007078173,0.0005793348,0.002556295,0.0006305199,0.05963258,0.001143681,0.7250791,0.1945239,0.0002116233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001877628,0.208189,0.7752472,0.006839428,0.0005947907,0.0002179198,0.0006383979,0.0005086896,0.005886983],"genre_scores_gemma":[0.05422509,0.5517264,0.3835244,0.002093929,0.00157796,0.001136869,0.001444579,0.0002291903,0.004041755],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9742759,"threshold_uncertainty_score":0.1360434,"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."}}