{"id":"W2935594703","doi":"10.1080/03461238.2019.1598482","title":"Multivariate Cox Hidden Markov models with an application to operational risk","year":2019,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Multivariate statistics; Computer science; Aggregate (composite); Econometrics; Flexibility (engineering); Expectation–maximization algorithm; Statistics; Mathematics; Machine learning; Maximum likelihood","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":[0.004148708,0.0007580872,0.0009689264,0.001045923,0.0005088326,0.001412082,0.001688169,0.00129307,0.002617555],"category_scores_gemma":[0.008843735,0.0006033754,0.001347487,0.00135008,0.0009032054,0.001499252,0.001090396,0.002533043,0.000344398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216734,"about_ca_system_score_gemma":0.001078641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01201147,"about_ca_topic_score_gemma":0.008613475,"domain_scores_codex":[0.9991178,0.0004611131,0.00004028747,0.000147678,0.0001279592,0.0001051876],"domain_scores_gemma":[0.992347,0.006242154,0.0006633515,0.0003152989,0.000287926,0.0001442315],"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":[0.00005665615,0.00004189696,0.003289321,0.0000535738,0.00007036416,0.0001607303,0.0001247878,0.8481309,0.0004267731,0.1327854,0.000987571,0.0138721],"study_design_scores_gemma":[0.000005098947,0.000007927752,0.0002745876,0.000005867446,0.000008234107,0.00001699772,0.000008883126,0.970334,0.00006566376,0.02889463,0.0003689218,0.0000091249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04432784,0.001166427,0.9507431,0.001089691,0.00007751438,0.00004742252,0.0005276231,0.0002905754,0.001729925],"genre_scores_gemma":[0.8770851,0.002292644,0.1124527,0.0001942409,0.0002934438,0.0002022777,0.0008900988,0.00009384485,0.006495541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01201147,"threshold_uncertainty_score":0.0238831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190263914356727,"score_gpt":0.2341132673792152,"score_spread":0.2150868759435425,"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."}}