{"id":"W6957946713","doi":"10.60692/29wad-fx472","title":"Estimating the Conditional Tail Expectation in the Case of Heavy‐Tailed Losses","year":2010,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Nonparametric statistics; Estimator; Asymptotic distribution; Measure (data warehouse); Moment (physics); Variable (mathematics); Normality; Conditional probability distribution","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":[],"consensus_categories":[],"category_scores_codex":[0.0009956479,0.00008533912,0.0001688348,0.0001543494,0.0001742496,0.0001007251,0.000152669,0.00006834098,0.00002557464],"category_scores_gemma":[0.0001294757,0.00005973501,0.0000560347,0.0001985697,0.00004305637,0.0005279977,0.0000186947,0.0001531986,0.000136244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002746108,"about_ca_system_score_gemma":0.00001705618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00021206,"about_ca_topic_score_gemma":0.00001873495,"domain_scores_codex":[0.9989015,0.00002359852,0.0008125461,0.00009272791,0.00004669313,0.0001229221],"domain_scores_gemma":[0.9991515,0.00003552129,0.0004722109,0.0002538445,0.0000707225,0.00001620149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004425599,0.000009429818,0.613612,0.0002954955,0.00001952531,0.00001215341,0.2802795,0.007479739,0.000001624877,0.09753571,0.0000653474,0.0006452081],"study_design_scores_gemma":[0.0008647827,0.0000336997,0.1628763,0.0000562136,0.000007607092,0.0002450647,0.0322472,0.8021288,0.00007081544,0.001143815,0.0001229275,0.0002027266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392229,0.000005922892,0.05829914,0.00009350583,0.0002947541,0.0002495874,0.0001520215,0.00001827868,0.001663874],"genre_scores_gemma":[0.9989803,5.243179e-8,0.0007914877,0.00006365054,0.00007489464,0.00005883946,0.00001984716,0.000003999688,0.000006869437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7946491,"threshold_uncertainty_score":0.2435923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04407552169734245,"score_gpt":0.2253286307998238,"score_spread":0.1812531091024813,"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."}}