{"id":"W4387140126","doi":"10.1007/s11222-023-10297-1","title":"Testing symmetry for bivariate copulas using Bernstein polynomials","year":2023,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Copula (linguistics); Bernstein polynomial; Mathematics; Multiplier (economics); Applied mathematics; Symmetry (geometry); Econometrics; Statistics","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":[0.0007115581,0.0001322886,0.0003332335,0.0001748882,0.0003895371,0.0001142324,0.00008830319,0.00006610286,0.000005322467],"category_scores_gemma":[0.0007596653,0.0001674911,0.00003868372,0.0003254741,0.00003198737,0.00005748041,0.0001035913,0.00008951395,0.00002258997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003626386,"about_ca_system_score_gemma":0.00002042977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006574238,"about_ca_topic_score_gemma":0.000007165374,"domain_scores_codex":[0.9986779,0.000009869528,0.0005650777,0.0003517503,0.00003032222,0.0003650801],"domain_scores_gemma":[0.9990246,0.0004698627,0.0002584273,0.0001271135,0.0000557325,0.00006422065],"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":[0.00001658718,0.00002522588,0.07015445,0.000232361,0.00004260975,0.000006689688,0.0006427129,0.004646261,0.000362449,0.8694556,0.0004900279,0.05392498],"study_design_scores_gemma":[0.0002777756,0.00003680189,0.008669813,0.00003778724,0.00000705214,0.00000167152,0.00004504874,0.8371426,0.00001570476,0.1530608,0.0005265481,0.0001783582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4338352,0.0002597808,0.5644386,0.0000290178,0.0003637479,0.0001464896,0.0006228055,0.00005870446,0.0002457057],"genre_scores_gemma":[0.8422126,0.00001454157,0.1574155,0.00004902908,0.0001848294,0.000002616103,0.00003711288,0.00002710771,0.00005670364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8324963,"threshold_uncertainty_score":0.6830087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081812010776814,"score_gpt":0.2943661963261016,"score_spread":0.1861849952484202,"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."}}