{"id":"W3123679122","doi":"10.1016/j.insmatheco.2015.06.001","title":"A general importance sampling algorithm for estimating portfolio loss probabilities in linear factor models","year":2015,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Copula (linguistics); Kullback–Leibler divergence; Conditional probability distribution; Exponential family; Conditional independence; Importance sampling; Algorithm; Mathematical optimization; Statistics; Computer science; Econometrics; Monte Carlo method","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005028524,0.001033222,0.002142173,0.001328586,0.0006806143,0.001407044,0.002645686,0.001798328,0.003563919],"category_scores_gemma":[0.01949089,0.001443172,0.001258413,0.001560707,0.0009116473,0.002354081,0.001721028,0.002468086,0.0009720899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008490855,"about_ca_system_score_gemma":0.001609786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007946835,"about_ca_topic_score_gemma":0.007610729,"domain_scores_codex":[0.998315,0.0007861278,0.0001043503,0.0003116292,0.0003753997,0.0001076176],"domain_scores_gemma":[0.9908415,0.007440338,0.0002351114,0.0004753,0.0008282544,0.0001795928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002090097,0.0001710785,0.001843617,0.000159222,0.0001504583,0.00009121645,0.0001030226,0.6946362,0.001991788,0.02991605,0.002986273,0.267742],"study_design_scores_gemma":[0.00002010448,0.00001529357,0.0001238667,0.000006552058,0.000008634171,0.00001794921,0.000002933908,0.99132,0.0002121878,0.007950141,0.0003162273,0.00000613583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002047122,0.00009879605,0.9974924,0.00002947651,0.00001574676,0.00002364836,0.00001873671,0.000139116,0.0001348079],"genre_scores_gemma":[0.0919371,0.0003313849,0.9046689,0.0001284031,0.0001571842,0.0003099881,0.0004478736,0.0001748057,0.001844412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007946835,"threshold_uncertainty_score":0.02659374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09822045834843861,"score_gpt":0.2697369148286425,"score_spread":0.1715164564802039,"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."}}