{"id":"W2133600798","doi":"10.1002/cjs.11146","title":"Positive quadrant dependence testing and constrained copula estimation","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds Wetenschappelijk Onderzoek","keywords":"Copula (linguistics); Resampling; Econometrics; Nonparametric statistics; Null hypothesis; Statistics; Statistical hypothesis testing; Mathematics; Parametric statistics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001074497,0.00008019265,0.0001382345,0.0001698709,0.0004573151,0.0001095218,0.0001156836,0.00004327647,0.0000506453],"category_scores_gemma":[0.001489256,0.00008381758,0.00002079301,0.0001959942,0.0004498487,0.0002903495,0.000005935203,0.0001434305,0.000004832039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001434076,"about_ca_system_score_gemma":0.0006924344,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0345479,"about_ca_topic_score_gemma":0.07598212,"domain_scores_codex":[0.9988801,0.000130167,0.0002808678,0.00006692605,0.0002759994,0.0003659146],"domain_scores_gemma":[0.9983752,0.0003372887,0.0002554088,0.00005492655,0.0003475958,0.0006295501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007469849,0.00002335559,0.6730022,0.00003762994,0.00007263936,0.0002833727,0.01292704,0.0001365545,0.00001517911,0.2338452,0.002276291,0.0773731],"study_design_scores_gemma":[0.0003939162,0.0001070123,0.973763,0.0001543926,0.0001436426,0.0001089013,0.005882308,0.001058884,0.00001247612,0.01591461,0.002181234,0.0002795829],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7010716,0.001961189,0.2598414,0.001376193,0.00301653,0.0007622914,0.001070283,0.00003190945,0.03086864],"genre_scores_gemma":[0.9352652,0.00003188444,0.06439207,0.0001252768,0.0001440755,8.128106e-7,0.000002890389,0.000006612698,0.00003111436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3007609,"threshold_uncertainty_score":0.9718812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03104600904206516,"score_gpt":0.2859623023398878,"score_spread":0.2549162932978227,"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."}}