{"id":"W2120185573","doi":"10.1017/asb.2015.6","title":"MODELING DEPENDENCE BETWEEN LOSS TRIANGLES WITH HIERARCHICAL ARCHIMEDEAN COPULAS","year":2015,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Copula (linguistics); Line of business; Property insurance; Tail dependence; Econometrics; Independence (probability theory); Computer science; Line (geometry); Mathematics; Mathematical economics; Business model; Actuarial science; Economics; Statistics; Insurance policy; General insurance","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":[0.009645287,0.001446519,0.002316462,0.002714456,0.001054786,0.002807246,0.003690706,0.002337459,0.005480389],"category_scores_gemma":[0.02502552,0.001612887,0.002594033,0.003127846,0.002418349,0.002807244,0.002569808,0.004091824,0.0008337196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003060331,"about_ca_system_score_gemma":0.001262023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04056594,"about_ca_topic_score_gemma":0.02159522,"domain_scores_codex":[0.9962555,0.001878421,0.0001454935,0.0008385407,0.0003534276,0.0005285855],"domain_scores_gemma":[0.9768861,0.01644586,0.003358244,0.001322687,0.00125976,0.0007272076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001202516,0.0001323454,0.0196815,0.00006754101,0.0002557398,0.000523879,0.000321688,0.8605393,0.0004350969,0.1076676,0.002741592,0.007513487],"study_design_scores_gemma":[0.00001092296,0.00001989768,0.00195456,0.00001118739,0.00001919639,0.00002364044,0.00004109227,0.9784471,0.00004979939,0.01905718,0.0003542138,0.00001116354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3442394,0.001246769,0.6456,0.001405002,0.000112461,0.0002384919,0.001795273,0.0003581466,0.00500444],"genre_scores_gemma":[0.9636838,0.0005179094,0.02959475,0.0002023346,0.000109757,0.0001892391,0.001183192,0.0001088217,0.004410258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04056594,"threshold_uncertainty_score":0.08065963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05682800852378835,"score_gpt":0.3065035672366355,"score_spread":0.2496755587128471,"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."}}