{"id":"W3041871434","doi":"10.1002/cjs.11558","title":"Copula‐based predictions in small area estimation","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Manitoba Health; University of Manitoba; Hospital for Sick Children; Statistics Canada","funders":"","keywords":"Small area estimation; Copula (linguistics); Estimator; Best linear unbiased prediction; Econometrics; Statistics; Parametric statistics; Multivariate statistics; Mean squared error; Unbiased Estimation; Mathematics; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005239245,0.0006647842,0.001015914,0.001288765,0.0003650549,0.00106514,0.001206294,0.0007076029,0.002062489],"category_scores_gemma":[0.02639511,0.0004263594,0.0006759024,0.001336947,0.000788802,0.001590961,0.00108571,0.001399756,0.0004892225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006326037,"about_ca_system_score_gemma":0.0007528924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008790399,"about_ca_topic_score_gemma":0.005358473,"domain_scores_codex":[0.9982539,0.001131886,0.00005059926,0.0002518524,0.0002344348,0.00007740584],"domain_scores_gemma":[0.987097,0.01032905,0.0009215485,0.0006971428,0.0008214186,0.0001337512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002641762,0.0000276525,0.003846947,0.00004060249,0.0000622027,0.00006634641,0.00006888429,0.9415674,0.000453314,0.02509643,0.001076583,0.02766712],"study_design_scores_gemma":[0.000001217734,0.000004305031,0.0005322418,0.000005329221,0.000002878735,0.000006227254,0.000007078341,0.9929374,0.0001049619,0.00624994,0.0001447413,0.000003739605],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02195104,0.0001796422,0.9767928,0.0001017856,0.00001176237,0.00001712897,0.00005694875,0.0001421409,0.0007466825],"genre_scores_gemma":[0.8473916,0.0004443042,0.14936,0.00009549637,0.00005541066,0.0001270503,0.0003253877,0.0001735689,0.002027108],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008790399,"threshold_uncertainty_score":0.02770811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1204992895991974,"score_gpt":0.3237608917039106,"score_spread":0.2032616021047132,"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."}}