{"id":"W1996718979","doi":"10.1007/s10687-014-0199-4","title":"Using B-splines for nonparametric inference on bivariate extreme-value copulas","year":2014,"lang":"en","type":"article","venue":"Extremes","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Institute of Engineering Research, Seoul National University; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Bivariate analysis; Extreme value theory; Estimator; Copula (linguistics); Nonparametric statistics; Tail dependence; Smoothing; Spline (mechanical); Inference; Vine copula; Quadratic equation; Econometrics; Applied mathematics; Statistics; Multivariate statistics; Computer science; Artificial intelligence; Geometry","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":[0.0007344068,0.0002186123,0.0004621957,0.0003945393,0.0002183015,0.00009155532,0.0002429628,0.0001387655,0.00006977811],"category_scores_gemma":[0.001584765,0.0002414614,0.0001680651,0.0003973292,0.00004020019,0.0001871589,0.00004846273,0.0001336923,0.0001601967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007655846,"about_ca_system_score_gemma":0.00002562006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004603211,"about_ca_topic_score_gemma":0.00002813362,"domain_scores_codex":[0.9983591,0.00001988973,0.000638528,0.0005372709,0.00005579982,0.0003893875],"domain_scores_gemma":[0.9988099,0.0003309755,0.0002967342,0.0004158823,0.00006624313,0.00008024106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008252166,0.000154591,0.05614929,0.00007049039,0.00002895128,5.909513e-7,0.0002339617,0.01259173,0.0001816201,0.9143811,0.0003523869,0.01577279],"study_design_scores_gemma":[0.0006026124,0.0001251854,0.008727531,0.0000440671,0.000009193513,5.338325e-7,0.00001018079,0.800818,0.0002155814,0.1615588,0.0275464,0.0003419236],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3983187,0.0008793426,0.5947147,0.0001742443,0.0008079693,0.0003061749,0.00006796497,0.00007029922,0.004660557],"genre_scores_gemma":[0.970778,0.00007471789,0.02804124,0.0002177544,0.0003801264,0.00002376282,0.00001309212,0.00003652075,0.0004347239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7882262,"threshold_uncertainty_score":0.984651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2081599130862025,"score_gpt":0.316859244648506,"score_spread":0.1086993315623034,"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."}}