{"id":"W4416940087","doi":"10.1007/s00245-025-10352-5","title":"A Parametric Approach to the Estimation of Convex Risk Functionals Based on Wasserstein Distance","year":2025,"lang":"en","type":"article","venue":"Applied Mathematics & Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Deutsche Forschungsgemeinschaft","keywords":"Parametric statistics; Nonparametric statistics; Probabilistic logic; Context (archaeology); Martingale (probability theory); Regular polygon; Margin (machine learning); Probability distribution; Parametrization (atmospheric modeling)","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":[0.005157871,0.001203397,0.002177753,0.00162123,0.0005023059,0.002205889,0.003107629,0.002355776,0.001861934],"category_scores_gemma":[0.03032581,0.001473424,0.001754173,0.001597516,0.002086005,0.00449595,0.004035729,0.00355399,0.0003909814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093724,"about_ca_system_score_gemma":0.001521795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002464021,"about_ca_topic_score_gemma":0.001760005,"domain_scores_codex":[0.9966063,0.001887451,0.000165734,0.0004762525,0.0007192426,0.0001449547],"domain_scores_gemma":[0.989256,0.008179119,0.0006978935,0.0008253612,0.0008390274,0.000202515],"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":[0.00005208869,0.00005383456,0.0005420649,0.0001236943,0.0001117824,0.00009767655,0.0001110613,0.7184554,0.002303625,0.2211986,0.0006607334,0.05628935],"study_design_scores_gemma":[0.000003289958,0.00002395637,0.00009530188,0.000008685775,0.000008984888,0.00002674576,0.00000503824,0.9569315,0.0003416938,0.0420678,0.0004729397,0.00001411855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001253997,0.00008669071,0.9983515,0.0000573242,0.000007755523,0.000005912427,0.000007880999,0.00002427142,0.0002046795],"genre_scores_gemma":[0.3379617,0.001183045,0.6548449,0.0001608926,0.000300829,0.0002324872,0.0002766055,0.0002944912,0.004745062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005157871,"threshold_uncertainty_score":0.02727777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03264989327758788,"score_gpt":0.3160187944073,"score_spread":0.2833689011297121,"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."}}