{"id":"W1983234616","doi":"10.1016/j.jspi.2012.01.008","title":"Large-sample confidence intervals for risk measures of location–scale families","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Mathematics; Statistics; Confidence interval; Expected shortfall; Gumbel distribution; Estimator; Risk measure; Value at risk; Standard deviation; Sample (material); Sample size determination; Econometrics; Extreme value theory; Risk management","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003229751,0.00008158548,0.0003081155,0.0001563842,0.0001020834,0.00007749497,0.000184164,0.00004940789,0.00004108337],"category_scores_gemma":[0.02121209,0.00005478182,0.00004110335,0.0001780921,0.0001221799,0.0004514765,0.00002934605,0.0001303991,0.000002878253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007828581,"about_ca_system_score_gemma":0.00007299442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000278778,"about_ca_topic_score_gemma":0.000004363146,"domain_scores_codex":[0.9982468,0.0001294641,0.0008019179,0.00009921878,0.0005466287,0.0001759326],"domain_scores_gemma":[0.9913114,0.006664596,0.0007317518,0.0001074085,0.001036334,0.0001484706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000322878,0.0001220694,0.8823417,0.00002216879,0.00003933144,0.000001314075,0.006954372,0.005057937,0.0000927805,0.03021012,0.006019681,0.0688156],"study_design_scores_gemma":[0.001099904,0.0009367863,0.745397,0.00030438,0.0001448494,0.00003908859,0.006874717,0.04187885,0.0008370385,0.183443,0.01875352,0.0002908635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08257696,0.000833377,0.9159306,0.00003993319,0.0001982487,0.00005265082,0.0001894298,0.00000306296,0.0001756788],"genre_scores_gemma":[0.9432433,0.0005174295,0.05609146,0.00003511376,0.00007826609,0.000001454086,0.000004098297,0.000003607517,0.00002527394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8606663,"threshold_uncertainty_score":0.9870327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.140887317388506,"score_gpt":0.4229066257676704,"score_spread":0.2820193083791643,"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."}}