{"id":"W4214891115","doi":"10.1111/biom.13652","title":"A Time-Heterogeneous D-Vine Copula Model for Unbalanced and Unequally Spaced Longitudinal Data","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vine copula; Copula (linguistics); Computer science; Homogeneous; Gaussian; Econometrics; Longitudinal data; Statistics; Mathematics; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.007563499,0.001245375,0.001675567,0.001867233,0.0006870934,0.00207344,0.003561924,0.00171884,0.003694717],"category_scores_gemma":[0.01944567,0.0009149562,0.00180585,0.002660391,0.001305016,0.002404288,0.002073087,0.002540947,0.0009278025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001322081,"about_ca_system_score_gemma":0.001668557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009170684,"about_ca_topic_score_gemma":0.006428724,"domain_scores_codex":[0.9956614,0.002208772,0.0001788709,0.001218783,0.0004046115,0.0003275113],"domain_scores_gemma":[0.994415,0.003530474,0.000718903,0.0006076965,0.0005451083,0.0001828517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002115757,0.0001380811,0.01164354,0.000249082,0.0005345019,0.0007349611,0.0005963115,0.4881761,0.001863793,0.4023924,0.004535065,0.08892455],"study_design_scores_gemma":[0.00002756842,0.00007407267,0.002378981,0.00003912621,0.00008654713,0.0001695328,0.00007301669,0.920049,0.0002963881,0.0727262,0.004034415,0.00004523884],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008873947,0.0003062349,0.9891823,0.0002230273,0.00005378054,0.00007131133,0.0002923879,0.0001183079,0.0008786565],"genre_scores_gemma":[0.5899332,0.001910282,0.3922809,0.0004652484,0.0002207191,0.001060883,0.001776789,0.0002311086,0.01212086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009170684,"threshold_uncertainty_score":0.04000008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2507653103219556,"score_gpt":0.4159281288095117,"score_spread":0.1651628184875561,"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."}}