{"id":"W4380740934","doi":"10.1080/10920277.2023.2202707","title":"GAMLSS for Longitudinal Multivariate Claim Count Models","year":2023,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Nonparametric statistics; Covariate; Econometrics; Multivariate statistics; Predictive power; Univariate; Parametric statistics; Generalized additive model; Semiparametric model; Predictive modelling; Computer science; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002017023,0.0002780847,0.0004775045,0.0004218286,0.001752503,0.0004541289,0.0007198911,0.00007222318,0.00009295351],"category_scores_gemma":[0.0002605295,0.0002643825,0.0004029357,0.001535692,0.0007416895,0.0005952138,0.00009373426,0.0004459745,0.0000917905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002655489,"about_ca_system_score_gemma":0.0004301092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00596993,"about_ca_topic_score_gemma":0.01119728,"domain_scores_codex":[0.9962956,0.0003152724,0.0005910626,0.0004533569,0.001193123,0.001151627],"domain_scores_gemma":[0.9979663,0.0002845235,0.00060538,0.0003003033,0.0004112325,0.0004322263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001622196,0.0006081414,0.5431156,0.00006821605,0.001451155,0.0004264548,0.02590039,0.02739467,0.00005161215,0.03175992,0.0744728,0.2931288],"study_design_scores_gemma":[0.00286538,0.0005351854,0.7912347,0.00004064179,0.0002940409,0.00001818659,0.007181221,0.00923954,0.000007269743,0.01184295,0.1756708,0.001070046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.927487,0.00005127861,0.05374331,0.002901148,0.005367537,0.001382435,0.0001660527,0.0004773392,0.008423877],"genre_scores_gemma":[0.9927768,0.0006342755,0.002454808,0.0004028447,0.003219198,0.00006744077,0.00002688195,0.00004566634,0.0003721302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2920588,"threshold_uncertainty_score":0.9999809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0620836293120554,"score_gpt":0.3540426059766704,"score_spread":0.291958976664615,"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."}}