{"id":"W2903241066","doi":"10.1002/cjs.11468","title":"Predictive assessment of copula models","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Copula (linguistics); Bivariate analysis; Multivariate statistics; Predictive power; Econometrics; Quantile; Statistics; Model selection; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.01215822,0.001180531,0.001586156,0.002919323,0.0006161509,0.002698651,0.001894199,0.001072809,0.003371309],"category_scores_gemma":[0.07393225,0.0005272251,0.001067629,0.002034354,0.001461673,0.002309492,0.001944241,0.002265959,0.0003879246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001639769,"about_ca_system_score_gemma":0.001220187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01284774,"about_ca_topic_score_gemma":0.005406803,"domain_scores_codex":[0.9965507,0.002129838,0.00009856263,0.000461965,0.0005721423,0.0001867451],"domain_scores_gemma":[0.9321194,0.05735125,0.003268857,0.003075667,0.003469737,0.000715073],"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":[0.00009840557,0.00004597495,0.01383006,0.00009514909,0.0001690356,0.0001520773,0.0001777411,0.9193795,0.0002972503,0.04381704,0.00196751,0.01997026],"study_design_scores_gemma":[0.000003416717,0.0000148515,0.0009697998,0.00002163858,0.00001319333,0.0000193446,0.00002079125,0.9834801,0.0001180673,0.01512706,0.0002021525,0.000009649393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2668234,0.001867102,0.7201523,0.001394786,0.0001151372,0.0001273396,0.0006557288,0.0007756791,0.008088541],"genre_scores_gemma":[0.9777266,0.0005031059,0.01988233,0.000104482,0.00007555773,0.0000739258,0.0005369565,0.0001051165,0.0009918878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01284774,"threshold_uncertainty_score":0.06429964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.136298318111266,"score_gpt":0.3868095029359923,"score_spread":0.2505111848247262,"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."}}