{"id":"W4307703628","doi":"10.3390/jrfm15110500","title":"On the Contaminated Weighted Exponential Distribution: Applications to Modeling Insurance Claim Data","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica","keywords":"Outlier; Kurtosis; Exponential family; Prior probability; Range (aeronautics); Econometrics; Computer science; Exponential function; Maximization; Expectation–maximization algorithm; Skewness; Bayesian probability; Exponential distribution; Bimodality; Statistics; Mathematics; Mathematical optimization; Maximum likelihood; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007808242,0.0009630081,0.001163307,0.001921505,0.000707128,0.001286356,0.001909688,0.002123955,0.001348925],"category_scores_gemma":[0.03865599,0.0004779646,0.001362908,0.002468429,0.001465259,0.002846512,0.001639224,0.002458629,0.0002892124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000828916,"about_ca_system_score_gemma":0.0008574793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006038706,"about_ca_topic_score_gemma":0.003982229,"domain_scores_codex":[0.9982628,0.0009259919,0.00009343601,0.0002779767,0.0003134438,0.0001263981],"domain_scores_gemma":[0.9750783,0.02097083,0.001397994,0.00102352,0.001263093,0.0002662106],"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":[0.00008070848,0.00006789624,0.005639577,0.00008791254,0.00009056333,0.00049006,0.0002489075,0.8632653,0.001383078,0.08816619,0.0009791908,0.03950065],"study_design_scores_gemma":[0.000004523324,0.00001401094,0.0006130628,0.00001480261,0.00000669357,0.0001081168,0.00003467212,0.9613867,0.0002303466,0.037095,0.000472969,0.00001896045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02923107,0.0003395134,0.9692075,0.0004051956,0.00002394221,0.00003407056,0.00009163161,0.000137247,0.0005299184],"genre_scores_gemma":[0.6821922,0.002015387,0.3115401,0.0003492096,0.0001693775,0.0002258127,0.0006318407,0.0001965741,0.002679541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007808242,"threshold_uncertainty_score":0.0412944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07522272787478275,"score_gpt":0.3200323398921081,"score_spread":0.2448096120173254,"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."}}