{"id":"W2047815880","doi":"10.1016/j.csda.2015.03.013","title":"Likelihood inference for small area estimation using data cloning","year":2015,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Manitoba; Manitoba Health","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Frequentist inference; Small area estimation; Generalized linear mixed model; Markov chain Monte Carlo; Statistics; Inference; Mathematics; Bayesian probability; Computer science; Econometrics; Sample size determination; Bayesian inference; Algorithm; Artificial intelligence; Estimator","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001709662,0.0002496376,0.0005570769,0.0002562338,0.000212708,0.0002771195,0.00115466,0.0000796863,0.00007421312],"category_scores_gemma":[0.01625816,0.0002460238,0.0000503791,0.0008198968,0.0001054653,0.0003733408,0.0007901227,0.0001470919,0.00001102667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009001158,"about_ca_system_score_gemma":0.0004645488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004004532,"about_ca_topic_score_gemma":0.0003845722,"domain_scores_codex":[0.9973478,0.0001890897,0.0007560112,0.0008052911,0.0005522666,0.0003495038],"domain_scores_gemma":[0.9902781,0.006738638,0.0004273283,0.001498052,0.0007955243,0.000262382],"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":[0.00007569882,0.0003234351,0.005557205,0.0002365392,0.001961116,0.0000225011,0.0003892801,0.07069951,0.000007194162,0.7868654,0.01005611,0.1238061],"study_design_scores_gemma":[0.000209745,0.00002208625,0.0005830718,0.00001807308,0.001473335,0.000001968954,0.00002161872,0.5256336,7.842125e-7,0.4717503,0.0001263913,0.0001590793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005562322,0.00003795237,0.9673049,0.00006517053,0.0001076044,0.0002480581,0.03158317,0.00005818014,0.00003867678],"genre_scores_gemma":[0.02261126,0.000004391569,0.9412825,0.00005719946,0.00008342712,0.00001085927,0.03591586,0.00002601588,0.000008497845],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4549341,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4510083262298539,"score_gpt":0.4857792121440956,"score_spread":0.03477088591424171,"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."}}