{"id":"W1968723219","doi":"10.1016/j.jspi.2005.11.011","title":"Estimating conditional tail expectation with actuarial applications in view","year":2008,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Indiana University Bloomington","keywords":"Estimator; Mathematics; Parametric statistics; Conditional expectation; Econometrics; Construct (python library); Asymptotic analysis; Mathematical economics; Applied mathematics; Statistics; Computer science","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":[0.0005090437,0.00007327901,0.0002097922,0.0001849487,0.0001401102,0.00007462577,0.0001070252,0.00003495093,0.00006610846],"category_scores_gemma":[0.001476181,0.00004736485,0.00001437803,0.0002660634,0.0001288788,0.0003784484,0.00001186071,0.0001686724,0.000004875829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001466518,"about_ca_system_score_gemma":0.0001406956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008329406,"about_ca_topic_score_gemma":0.000001533864,"domain_scores_codex":[0.9985858,0.00006686452,0.0005741254,0.0001224131,0.0005519862,0.00009881822],"domain_scores_gemma":[0.9972866,0.00188208,0.0003780424,0.00006307029,0.0002983824,0.00009178329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002685356,0.0001563766,0.7886259,0.00001118708,0.00002341414,0.0002081592,0.004956636,0.137962,0.00006012395,0.02583238,0.002466233,0.03942908],"study_design_scores_gemma":[0.001004771,0.0004314474,0.8533356,0.0001208683,0.00001934758,0.0003566524,0.0009882289,0.09017067,0.00002090289,0.05226097,0.001110339,0.0001802765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1997323,0.00006066631,0.7996389,0.00005839485,0.0000411404,0.00004692522,0.00001362968,0.000003406787,0.0004046411],"genre_scores_gemma":[0.8921151,0.00003713758,0.1077067,0.0000291706,0.00007429269,0.0000033378,0.00001448369,0.000002816822,0.00001705406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6923827,"threshold_uncertainty_score":0.1931482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0892378011964783,"score_gpt":0.4007101723045575,"score_spread":0.3114723711080792,"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."}}