{"id":"W2950163193","doi":"10.2139/ssrn.2912029","title":"Vine Copula Models with GLM and Sparsity","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vine copula; Copula (linguistics); Econometrics; Statistics; Mathematics; Computer science","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":[0.001181224,0.0001216,0.0002699096,0.00007558738,0.0007556137,0.0001853602,0.0002717726,0.00006923486,0.00001264085],"category_scores_gemma":[0.00005554921,0.0001176555,0.00005405694,0.00003156352,0.00007230037,0.0005686824,0.00005886851,0.0008188145,0.00002079983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000232098,"about_ca_system_score_gemma":0.0002076018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008312296,"about_ca_topic_score_gemma":0.001816146,"domain_scores_codex":[0.9983976,0.000006965703,0.0002712895,0.0002431469,0.00004003756,0.001040979],"domain_scores_gemma":[0.999231,0.000008892959,0.0003467564,0.0003029518,0.00003513338,0.00007529712],"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":[0.00006875955,0.00002879635,0.07735568,0.000004697357,0.00004838193,0.00000262234,0.0001233274,0.0002654936,0.000003190981,0.9154654,0.0000171146,0.006616506],"study_design_scores_gemma":[0.000748665,0.0001922314,0.01420141,0.00001466895,0.00000905685,0.00009086975,0.00007492034,0.028691,0.000005542007,0.9546661,0.001119548,0.0001859946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8734642,0.006011175,0.1149492,0.0007139789,0.0001083631,0.00007670547,0.0000110049,0.00001288034,0.00465255],"genre_scores_gemma":[0.9926131,0.005927976,0.0005128328,0.00003477598,0.000144426,0.000001484287,0.000001182399,0.00001599426,0.000748265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1191489,"threshold_uncertainty_score":0.5811646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0316615518911651,"score_gpt":0.2171925471003705,"score_spread":0.1855309952092054,"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."}}