{"id":"W4390962405","doi":"10.48550/arxiv.2401.07724","title":"A non-parametric estimator for Archimedean copulas under flexible censoring scenarios and an application to claims reserving","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Estimator; Censoring (clinical trials); Bivariate analysis; Parametric statistics; Computer science; Univariate; Econometrics; Model selection; Goodness of fit; Nonparametric statistics; Parametric model; Statistics; Mathematics; Multivariate statistics; Artificial intelligence; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01319882,0.000759465,0.001364359,0.001583269,0.000590063,0.001315262,0.002590688,0.001558771,0.001991621],"category_scores_gemma":[0.05285232,0.0005598283,0.001376827,0.00209836,0.0009967578,0.001544422,0.001808035,0.002836035,0.0003228702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008890615,"about_ca_system_score_gemma":0.00176104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005031767,"about_ca_topic_score_gemma":0.004834856,"domain_scores_codex":[0.9963824,0.002757678,0.0001085558,0.0002948266,0.0003277162,0.0001288282],"domain_scores_gemma":[0.9665719,0.02716713,0.001951329,0.002736479,0.001235456,0.0003377619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007880509,0.0001845852,0.0137258,0.0001721577,0.0002465144,0.0004254838,0.0002760231,0.7675454,0.001639819,0.1350753,0.002899127,0.07773109],"study_design_scores_gemma":[0.00001667865,0.00003008687,0.001171999,0.00001700477,0.00001434645,0.00005365999,0.0000308543,0.9773532,0.0002531359,0.02041201,0.0006308226,0.00001616828],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01278365,0.0001261728,0.9862809,0.0001839876,0.00001259805,0.00005799566,0.0001030141,0.0001253621,0.0003262541],"genre_scores_gemma":[0.4667214,0.0006182551,0.5294371,0.0002330021,0.0001320345,0.0005699209,0.0008935886,0.0001620193,0.001232762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01319882,"threshold_uncertainty_score":0.06980282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09043082734547293,"score_gpt":0.2917463039765739,"score_spread":0.2013154766311009,"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."}}