{"id":"W4406061580","doi":"10.1016/j.insmatheco.2024.12.008","title":"Uncertainty in heteroscedastic Bayesian model averaging","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Concordia University","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Heteroscedasticity; Econometrics; Bayesian probability; Bayesian inference; Mathematics; Statistics; Computer science","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":[0.02335311,0.001023421,0.002322653,0.002291174,0.0008018638,0.003356866,0.002233861,0.001586778,0.001471813],"category_scores_gemma":[0.05717853,0.0008923502,0.001618953,0.002422172,0.001905582,0.004025987,0.002672057,0.00182586,0.0002327958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001975787,"about_ca_system_score_gemma":0.001504024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005881024,"about_ca_topic_score_gemma":0.004222423,"domain_scores_codex":[0.9881352,0.007168854,0.0006047095,0.001507449,0.002264037,0.0003196456],"domain_scores_gemma":[0.9694744,0.02503856,0.001874562,0.001701906,0.001590355,0.0003202685],"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":[0.00006221793,0.0000316998,0.002507897,0.000177378,0.0002764206,0.0001407021,0.0002177714,0.7464672,0.0005079249,0.1918757,0.0008963573,0.05683881],"study_design_scores_gemma":[0.000008196931,0.00002500697,0.0005974022,0.00003184731,0.00002766903,0.00004976436,0.00001833841,0.8324988,0.0003184366,0.165594,0.0008048345,0.00002568078],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01648586,0.001411125,0.9801175,0.0003647977,0.00003119957,0.00002538245,0.00007591723,0.0001294567,0.001358785],"genre_scores_gemma":[0.7222352,0.002164857,0.2722116,0.0002369432,0.0002475571,0.000196259,0.0005093357,0.0002011226,0.001997078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02335311,"threshold_uncertainty_score":0.1235046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03707278019904615,"score_gpt":0.3150349946419129,"score_spread":0.2779622144428667,"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."}}