{"id":"W1650463848","doi":"10.1002/sta4.64","title":"Additive models for conditional copulas","year":2014,"lang":"en","type":"preprint","venue":"Stat","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Covariate; Conditional probability distribution; Bivariate analysis; Econometrics; Inference; Joint probability distribution; Conditional dependence; Conditional expectation; Conditional probability; Conditional variance; Computer science; Marginal distribution; Bayesian inference; Mathematics; Bayesian probability; Statistics; Artificial intelligence; Random variable; Autoregressive conditional heteroskedasticity","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004170588,0.0002315601,0.0005885335,0.0001484078,0.0001250054,0.00007155555,0.0002318541,0.0003014231,0.0002140365],"category_scores_gemma":[0.0001576188,0.0002990403,0.0002809672,0.00003962385,0.00005909166,0.0001151192,0.0001727028,0.0003232516,0.000190013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001336785,"about_ca_system_score_gemma":0.0000699275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002215673,"about_ca_topic_score_gemma":0.00003740445,"domain_scores_codex":[0.9983614,0.00001032807,0.0006289736,0.0006497248,0.00004137775,0.0003081728],"domain_scores_gemma":[0.9989418,0.0001145845,0.0004063262,0.0003558151,0.0001071964,0.00007421043],"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":[0.00004817227,0.00004833107,0.0003466574,0.0001543548,0.00006526268,6.617375e-7,0.000364191,0.04825819,4.129415e-7,0.9270459,0.02218117,0.001486684],"study_design_scores_gemma":[0.0002182614,0.00002392436,0.0002132799,0.00001950993,0.00000542347,1.845078e-7,0.000008799967,0.386202,0.000005433801,0.5724327,0.04067101,0.0001994757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01646209,0.001086887,0.9452341,0.0002568059,0.00096327,0.0006262569,0.02438902,0.00005703099,0.01092455],"genre_scores_gemma":[0.9826196,0.0002236336,0.01086449,0.0002929487,0.0005402284,0.0003037124,0.004078323,0.00004900877,0.001028096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9661575,"threshold_uncertainty_score":0.9999462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07907764633782566,"score_gpt":0.2693837230659103,"score_spread":0.1903060767280846,"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."}}