{"id":"W3124874581","doi":"10.1016/j.insmatheco.2016.08.006","title":"Stochastic loss reserving with dependence: A flexible multivariate Tweedie approach","year":2016,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Australian Research Council; University of New South Wales","keywords":"Multivariate statistics; Econometrics; Flexibility (engineering); Parametric statistics; Portfolio; Marginal distribution; Computer science; Transparency (behavior); Mathematical optimization; Mathematics; Applied mathematics; Statistics; Random variable; Economics","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":[0.005013718,0.001325368,0.002728223,0.002109252,0.0008526028,0.003414482,0.004407635,0.002999678,0.005648038],"category_scores_gemma":[0.01660939,0.001346127,0.001922474,0.001817925,0.00328578,0.007479997,0.003152868,0.004757951,0.000599645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144059,"about_ca_system_score_gemma":0.001155862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001955779,"about_ca_topic_score_gemma":0.001639838,"domain_scores_codex":[0.9982715,0.0007095447,0.0000988981,0.0002744909,0.0003724787,0.0002731284],"domain_scores_gemma":[0.9918982,0.004801748,0.001156112,0.0008542793,0.0006976312,0.000592118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007988995,0.0001086826,0.0005881215,0.00008427284,0.00009111372,0.0003042641,0.00009115091,0.445275,0.001078747,0.5350827,0.001347189,0.0158689],"study_design_scores_gemma":[0.000005692524,0.00001102339,0.00008373237,0.000006334707,0.00001205284,0.000033396,0.000009081477,0.9342992,0.00009598673,0.06514676,0.0002802733,0.00001650309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01695504,0.0004216824,0.9786582,0.0006575379,0.0001289352,0.00002208562,0.00004963548,0.00007197449,0.003034912],"genre_scores_gemma":[0.7855023,0.002062344,0.1795181,0.0004533011,0.0007661368,0.0001818542,0.0001874676,0.0003648146,0.03096369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005648038,"threshold_uncertainty_score":0.02651542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1034963388098663,"score_gpt":0.3167575332757958,"score_spread":0.2132611944659294,"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."}}