{"id":"W2919738624","doi":"10.1017/asb.2019.3","title":"JOINT LIFE INSURANCE PRICING USING EXTENDED MARSHALL–OLKIN MODELS","year":2019,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Life insurance; Econometrics; Joint probability distribution; Residual; Actuarial science; Joint (building); Economics; Computer science; Statistics; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00191288,0.0002782953,0.0004079723,0.0002059835,0.0005126448,0.0001770665,0.0005163064,0.000147407,0.001141656],"category_scores_gemma":[0.0002378507,0.0002969385,0.0001976143,0.0005445658,0.0002383093,0.000196175,0.0001961063,0.0003059487,0.0007664167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000148229,"about_ca_system_score_gemma":0.0001396203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004425746,"about_ca_topic_score_gemma":0.0002224838,"domain_scores_codex":[0.9966017,0.0004189294,0.0005482423,0.0006371331,0.0009655583,0.0008284179],"domain_scores_gemma":[0.9986566,0.0001239439,0.0003018999,0.0005220202,0.0001796762,0.0002159097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002776968,0.00102987,0.5783075,0.0006755664,0.0005260396,0.0001641207,0.02143925,0.05677332,0.002234588,0.2688724,0.0222692,0.04743044],"study_design_scores_gemma":[0.002841472,0.0001840991,0.6400177,0.0007981088,0.0001330906,0.000008157591,0.008459881,0.01632655,0.0002219203,0.01283555,0.3157679,0.002405545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8667914,0.0004645205,0.002708319,0.001979689,0.001055249,0.0009958185,0.00001170343,0.0002594602,0.1257339],"genre_scores_gemma":[0.989691,0.0001447608,0.006089751,0.001001348,0.0003307318,0.00002761731,0.000004072761,0.0000462993,0.002664398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2934987,"threshold_uncertainty_score":0.9999483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0402189907330563,"score_gpt":0.2781090556575537,"score_spread":0.2378900649244974,"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."}}