{"id":"W3148904637","doi":"10.2139/ssrn.3740215","title":"LRMoE: An R Package for Flexible Actuarial Loss Modelling Using Mixture of Experts Regression Model","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistics; Regression; Regression analysis; Econometrics; R package; Computer science; Mathematics","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":[0.00346421,0.001368996,0.001286296,0.001087842,0.0003103843,0.001432732,0.002628417,0.001383276,0.05381605],"category_scores_gemma":[0.0192631,0.001170448,0.002196966,0.0007400982,0.000313687,0.001397188,0.001439868,0.002518234,0.02713759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004482737,"about_ca_system_score_gemma":0.001153718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004672681,"about_ca_topic_score_gemma":0.006342266,"domain_scores_codex":[0.9990274,0.0005068579,0.00007566966,0.0001533822,0.0001662469,0.0000704967],"domain_scores_gemma":[0.9944779,0.004134154,0.0003302383,0.0005677617,0.000387089,0.0001028797],"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":[0.0006028591,0.0002004475,0.007150372,0.001439329,0.00181345,0.0005683097,0.0003073807,0.3987019,0.002333365,0.05008036,0.2812052,0.2555971],"study_design_scores_gemma":[0.0001697737,0.00007341916,0.001424015,0.0001545006,0.0001590184,0.0003214501,0.00003257369,0.8888425,0.001861055,0.0342775,0.07258334,0.0001008813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.003565137,0.0002811947,0.928535,0.0002394411,0.0001550484,0.0001022967,0.01207467,0.05311888,0.00192848],"genre_scores_gemma":[0.08525696,0.0005620153,0.8457233,0.0004296227,0.0001863472,0.00110718,0.02012878,0.03387673,0.01272917],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05381605,"threshold_uncertainty_score":0.1800326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06570594852370867,"score_gpt":0.3442578730376465,"score_spread":0.2785519245139378,"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."}}