{"id":"W3160236489","doi":"10.2139/ssrn.3317868","title":"Nonparametric Inference for VaR, CTE, and Expectile with High-order Precision","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Inference; Econometrics; Nonparametric statistics; Statistics; Mathematics; Economics; Computer science; Artificial intelligence","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":[0.03791041,0.001625857,0.003705651,0.002855263,0.001617049,0.004660123,0.004534166,0.003346001,0.007522732],"category_scores_gemma":[0.206471,0.001542776,0.003072127,0.003178952,0.005901203,0.008641914,0.004009423,0.009474957,0.0009928524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00258209,"about_ca_system_score_gemma":0.004450039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006036603,"about_ca_topic_score_gemma":0.007764154,"domain_scores_codex":[0.9805332,0.01151296,0.0009377765,0.003503856,0.002360722,0.001151467],"domain_scores_gemma":[0.7650599,0.200809,0.007018789,0.02139821,0.004419987,0.001294108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002785479,0.0001633769,0.008525164,0.0003766489,0.0005705959,0.0003326505,0.000276782,0.1665732,0.000734098,0.7477845,0.005170901,0.0692136],"study_design_scores_gemma":[0.00006021532,0.00007892112,0.002286152,0.00008379634,0.00007697795,0.0001293691,0.00005147744,0.4633691,0.0006608161,0.5312976,0.001857573,0.00004800202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009314324,0.0005276725,0.9880684,0.0005606523,0.0000906539,0.00003391655,0.0002712405,0.0003389672,0.0007941301],"genre_scores_gemma":[0.6025186,0.001931459,0.3781675,0.000849021,0.00109289,0.0005399455,0.002546274,0.0005910239,0.01176328],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03791041,"threshold_uncertainty_score":0.2004918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02336029057424754,"score_gpt":0.3328307210433129,"score_spread":0.3094704304690654,"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."}}