{"id":"W1501966111","doi":"10.1016/j.jmva.2008.05.004","title":"Testing for Equality between Two Copulas","year":2007,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Monte Carlo method; Mathematics; Test statistic; Statistic; Inference; Econometrics; Applied mathematics; Multiplier (economics); Central limit theorem; Copula (linguistics); Statistical hypothesis testing; Statistics; Computer science; Economics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03359288,0.0009993246,0.001636505,0.001929521,0.0009581945,0.00370031,0.002537483,0.002044401,0.009163252],"category_scores_gemma":[0.19783,0.0007165444,0.002354585,0.001338507,0.003219268,0.005603199,0.00531672,0.003720535,0.0009870662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005856717,"about_ca_system_score_gemma":0.001443528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004902897,"about_ca_topic_score_gemma":0.0001979157,"domain_scores_codex":[0.9648542,0.02054824,0.001988666,0.005738943,0.003791215,0.003078678],"domain_scores_gemma":[0.5990584,0.3652785,0.007996842,0.01536941,0.007246229,0.005050464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.008634835,0.002336261,0.4856264,0.0007427931,0.004294917,0.00370906,0.00389289,0.03894822,0.02008691,0.1764212,0.006704263,0.2486023],"study_design_scores_gemma":[0.001625142,0.005776218,0.2261187,0.0003317713,0.001342968,0.004862688,0.005209321,0.4602196,0.01816482,0.2696565,0.006343139,0.0003491064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8275982,0.0003596588,0.1630503,0.001056496,0.0002251705,0.000107848,0.0004228156,0.0002585362,0.006920996],"genre_scores_gemma":[0.989237,0.00004455048,0.009783937,0.0001086626,0.00007698719,0.00003277318,0.0004576004,0.00003781261,0.0002206535],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03359288,"threshold_uncertainty_score":0.1776583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1293577495992285,"score_gpt":0.3461489603644888,"score_spread":0.2167912107652603,"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."}}