{"id":"W2955494793","doi":"10.1002/sim.8283","title":"Dependence modeling for multi‐type recurrent events via copulas","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Copula (linguistics); Inference; Multivariate statistics; Econometrics; Marginal model; Computer science; Poisson distribution; Marginal likelihood; Marginal distribution; Statistics; Mathematics; Artificial intelligence; Machine learning; Regression analysis; Random variable; Bayesian probability","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":[0.01421441,0.001630454,0.002264446,0.00229021,0.000759714,0.002871097,0.004169282,0.002186118,0.004189324],"category_scores_gemma":[0.03754616,0.001504451,0.00314666,0.002032153,0.002929969,0.004623058,0.003367774,0.00437629,0.0008051697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577674,"about_ca_system_score_gemma":0.001157848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004623394,"about_ca_topic_score_gemma":0.00380952,"domain_scores_codex":[0.9948965,0.002688258,0.0002488205,0.001021359,0.0007074996,0.0004375866],"domain_scores_gemma":[0.9719821,0.02093725,0.003106357,0.00222801,0.001218188,0.0005282012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009727981,0.0001100599,0.007730406,0.000189759,0.0004328698,0.0009339885,0.0006243724,0.3633816,0.00175541,0.5980116,0.001679647,0.02505287],"study_design_scores_gemma":[0.000009814226,0.0000255675,0.0009128976,0.00002470609,0.00005093057,0.0001178996,0.00004548681,0.8632837,0.0002514179,0.1343812,0.0008657871,0.00003057949],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01049225,0.0002765314,0.9880884,0.0001625693,0.00002411293,0.00003373729,0.000108651,0.000115039,0.0006987164],"genre_scores_gemma":[0.7396438,0.002279813,0.244808,0.0004515644,0.000310045,0.0005645085,0.001099501,0.0004056329,0.0104373],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01421441,"threshold_uncertainty_score":0.07517391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2234552633684189,"score_gpt":0.4858875159557937,"score_spread":0.2624322525873747,"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."}}