{"id":"W1996388751","doi":"10.1016/j.jmva.2013.05.001","title":"Factor copula models for multivariate data","year":2013,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":181,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Mathematics; Multivariate statistics; Vine copula; Akaike information criterion; Bivariate analysis; Tail dependence; Econometrics; Latent variable; Statistics; Factor analysis; Applied mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001181031,0.0002124952,0.001067955,0.0007254379,0.0001391426,0.0001580328,0.0008302746,0.0001570418,0.0003160806],"category_scores_gemma":[0.0006841787,0.00019929,0.0006165489,0.00061314,0.00002794054,0.001550193,0.0001460506,0.0002401918,0.0000644834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009093798,"about_ca_system_score_gemma":0.00004510494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003575511,"about_ca_topic_score_gemma":0.00007419107,"domain_scores_codex":[0.997399,0.00003482159,0.001682677,0.0004358333,0.00009848132,0.0003491757],"domain_scores_gemma":[0.9969788,0.0002193004,0.001502569,0.0007509869,0.000383828,0.0001645557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009431424,0.002904681,0.1685222,0.0003208847,0.02958408,0.0000383604,0.009689958,0.5688284,0.003192404,0.1551684,0.007297325,0.0535102],"study_design_scores_gemma":[0.0009748561,0.00005614702,0.02303169,0.00001238128,0.0003233552,0.00000146379,0.00005073374,0.9341797,0.00002298072,0.03864563,0.002472513,0.0002285472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1864731,0.0008737964,0.8108453,0.000417331,0.0002982651,0.0002477985,0.0005696858,0.00001314967,0.0002615669],"genre_scores_gemma":[0.9495361,0.0001455109,0.04961823,0.00008902396,0.0002339137,0.000008632352,0.00006193111,0.00002631213,0.0002802868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7630631,"threshold_uncertainty_score":0.8126811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1364904153291479,"score_gpt":0.3067539562389998,"score_spread":0.1702635409098519,"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."}}