{"id":"W4231691288","doi":"10.2143/ast.37.2.2024077","title":"A Primer on Copulas for Count Data","year":2007,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":328,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Eidgenössische Technische Hochschule Zürich","keywords":"Copula (linguistics); Inference; Econometrics; Transposition (logic); Mathematics; Mathematical economics; Statistical physics; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001624848,0.0001200109,0.0002403281,0.00008833225,0.0001158802,0.00003191227,0.000343656,0.00008929674,0.0003692547],"category_scores_gemma":[0.0006677264,0.0001398679,0.00005476048,0.00008124379,0.00002847213,0.00003839003,0.00008926653,0.0001195374,0.001318038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004773128,"about_ca_system_score_gemma":0.00001386215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003339348,"about_ca_topic_score_gemma":0.00003843579,"domain_scores_codex":[0.998664,0.000004109818,0.0004877853,0.0004783616,0.00003725125,0.0003284892],"domain_scores_gemma":[0.998948,0.0002020821,0.0001588498,0.0005989844,0.0000319085,0.00006020219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007296145,0.0004750935,0.08750653,0.0001149452,0.00007301019,0.00001005,0.0004725875,0.0003563612,0.00004947228,0.5736684,0.2935095,0.04303442],"study_design_scores_gemma":[0.0004785195,0.00008987107,0.01314376,0.00001868441,0.000003704443,7.888745e-7,0.00001592037,0.006985184,0.00005860832,0.004934477,0.974083,0.0001874631],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3498088,0.002507459,0.5847106,0.005518268,0.001770571,0.001169923,0.001531105,0.0001612198,0.05282206],"genre_scores_gemma":[0.9805509,0.00003721335,0.01597564,0.000880831,0.0003928546,0.00001456785,0.0001492807,0.00003390667,0.001964815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6805735,"threshold_uncertainty_score":0.9994596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147412155229859,"score_gpt":0.286391501383458,"score_spread":0.1716502858604722,"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."}}