{"id":"W2017784796","doi":"10.1002/cjs.10078","title":"Mean squared error estimators of small area means using survey weights","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mean squared error; Estimator; Mathematics; Statistics; Small area estimation; Bias of an estimator; Best linear unbiased prediction; Efficient estimator; Consistency (knowledge bases); Minimum-variance unbiased estimator; Econometrics; Computer science; Discrete mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00124665,0.0002035903,0.0005697083,0.000303156,0.0001253106,0.0000585466,0.0003556985,0.0001332135,0.000539652],"category_scores_gemma":[0.008545387,0.0001772912,0.00007611703,0.0002323766,0.0003176944,0.00006710193,0.00001309532,0.0005134351,0.000002719552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008104803,"about_ca_system_score_gemma":0.001756044,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006904661,"about_ca_topic_score_gemma":0.15654,"domain_scores_codex":[0.9980595,0.0002180674,0.0009100616,0.0001418015,0.0002795446,0.000391034],"domain_scores_gemma":[0.9945626,0.002558477,0.0006966388,0.0002759676,0.001023902,0.00088236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000031918,0.00005710165,0.03015332,0.000179482,0.0001163195,0.0004010462,0.001164033,0.00001439362,0.0004093083,0.9567891,0.002476843,0.008207109],"study_design_scores_gemma":[0.0005061898,0.000206602,0.02970043,0.0002281634,0.0002121988,0.0002557567,0.0001730096,0.009548335,0.0004484638,0.9580615,0.0003179655,0.0003413829],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1087956,0.00002037906,0.8880295,0.00002496424,0.0007672875,0.00009166024,0.002028245,0.000004437074,0.0002379553],"genre_scores_gemma":[0.2122003,0.000001899932,0.7876576,0.00002043282,0.00006581126,4.218756e-7,0.000007271153,0.00002888918,0.00001739982],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1496353,"threshold_uncertainty_score":0.999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1284675501803468,"score_gpt":0.3443287159617772,"score_spread":0.2158611657814303,"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."}}