{"id":"W2046113245","doi":"10.1002/cjs.5550350205","title":"Nonparametric estimation of copula functions for dependence modelling","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":165,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Iowa State University","keywords":"Copula (linguistics); Estimator; Kernel smoother; Mathematics; Nonparametric statistics; Smoothing; Econometrics; Joint probability distribution; Statistics; Parametric statistics; Kernel method; Computer science; Artificial intelligence; Support vector machine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001143958,0.00008847268,0.0002550539,0.000350522,0.00009229029,0.00002316976,0.0001306145,0.00006316817,0.00008054524],"category_scores_gemma":[0.007203643,0.00008378903,0.00004893887,0.0002623895,0.00008536138,0.0000586881,0.000003660892,0.000154219,0.000001907649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001034056,"about_ca_system_score_gemma":0.0005918656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006162726,"about_ca_topic_score_gemma":0.001843569,"domain_scores_codex":[0.9987764,0.00002871192,0.0006846649,0.00007537246,0.0001927915,0.0002420986],"domain_scores_gemma":[0.9947494,0.003567956,0.0004369942,0.000107424,0.0007660704,0.0003721995],"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":[0.00004436573,0.00003665382,0.0005646533,0.0002184222,0.00005330117,0.00004769777,0.0003090166,0.01416755,0.00004124368,0.8838083,0.004853813,0.09585496],"study_design_scores_gemma":[0.0003526967,0.000321902,0.0006773074,0.0001175079,0.0001442081,0.00006127384,0.0001927501,0.2101814,0.0002904242,0.787096,0.0004302752,0.0001342286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007611195,0.00007113052,0.9909383,0.00002058772,0.000347928,0.0001178231,0.0005735465,0.000002612631,0.0003168759],"genre_scores_gemma":[0.3667031,0.000003639181,0.6331857,0.00001357136,0.00003707637,7.52236e-7,0.000003321306,0.000009123955,0.0000437526],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3590919,"threshold_uncertainty_score":0.8623953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1481917443454699,"score_gpt":0.3613164904895311,"score_spread":0.2131247461440612,"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."}}