{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02807358,0.0007569827,0.001675633,0.002474466,0.0004161155,0.001678379,0.001917866,0.001280406,0.002328378],"category_scores_gemma":[0.1481846,0.0007411509,0.001074443,0.004315784,0.001526661,0.002707183,0.002045084,0.001744645,0.000535499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215407,"about_ca_system_score_gemma":0.0014793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007337091,"about_ca_topic_score_gemma":0.005167226,"domain_scores_codex":[0.9721248,0.0220996,0.0007193161,0.002605888,0.002091841,0.0003585604],"domain_scores_gemma":[0.9091461,0.06969776,0.005583076,0.008793854,0.006354135,0.0004250326],"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":[0.0003137738,0.0001395945,0.03404528,0.0004100734,0.001140675,0.000105629,0.0004482142,0.4745004,0.0008622532,0.2127612,0.006640567,0.2686323],"study_design_scores_gemma":[0.00009141251,0.0001571842,0.01037987,0.0001413511,0.0001139853,0.0000801784,0.0001138654,0.7954482,0.00115719,0.1854796,0.006771011,0.00006610515],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01570639,0.0003344648,0.9825449,0.0001589992,0.00004655194,0.00006248985,0.000164037,0.0001513215,0.0008309571],"genre_scores_gemma":[0.4334573,0.0006535532,0.560982,0.0002015094,0.0001609904,0.0006431159,0.0009832854,0.0001156013,0.002802586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02807358,"threshold_uncertainty_score":0,"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."}}