{"id":"W3088854618","doi":"10.1111/jori.12327","title":"Wishart‐gamma random effects models with applications to nonlife insurance","year":2020,"lang":"en","type":"article","venue":"Journal of Risk & Insurance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Centre National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Wishart distribution; Econometrics; Censoring (clinical trials); Multivariate statistics; Random effects model; Context (archaeology); Diagonal; Computer science; Statistics; Actuarial science; Mathematics; Economics; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009581913,0.001431077,0.001716644,0.00166776,0.0005322037,0.002252093,0.002732037,0.002322043,0.005611109],"category_scores_gemma":[0.02410874,0.00120551,0.002306914,0.002205462,0.002358477,0.002148283,0.00186322,0.003821188,0.000880524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001534341,"about_ca_system_score_gemma":0.001454247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01698321,"about_ca_topic_score_gemma":0.01275418,"domain_scores_codex":[0.9976006,0.001579849,0.0001064207,0.0002862722,0.0002415341,0.0001852751],"domain_scores_gemma":[0.9753456,0.01981694,0.001996323,0.001171468,0.001168646,0.000501063],"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":[0.00004838074,0.0000623562,0.002374903,0.00007613842,0.0001514889,0.0004358746,0.0001750747,0.5718768,0.0002608887,0.4107611,0.001851428,0.01192569],"study_design_scores_gemma":[0.00001310448,0.00002345104,0.0004605104,0.0000224374,0.00002693965,0.00005143362,0.00003371868,0.8874994,0.00009247526,0.1106465,0.001105117,0.00002480571],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01470053,0.001130732,0.9805735,0.0008240668,0.00009470457,0.00005602237,0.0002930979,0.0002064435,0.002121057],"genre_scores_gemma":[0.77732,0.004588712,0.1884462,0.0005787438,0.0005344658,0.000608837,0.000947689,0.0002750272,0.02670029],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01698321,"threshold_uncertainty_score":0.05067456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177667019250133,"score_gpt":0.2771539020149482,"score_spread":0.2593872000899349,"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."}}