{"id":"W1986211349","doi":"10.1016/j.csda.2012.03.011","title":"Bootstrap variance estimation with survey data when estimating model parameters","year":2012,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Sampling design; Statistics; Sampling (signal processing); Variance (accounting); Simple random sample; Stratified sampling; Mathematics; Inference; Population; Econometrics; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.04195685,0.001309248,0.002270601,0.003412984,0.001244726,0.002152989,0.003658028,0.002594737,0.003322963],"category_scores_gemma":[0.2849794,0.001654493,0.0022052,0.004868048,0.002446605,0.004262309,0.002609405,0.003417789,0.00119132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008864483,"about_ca_system_score_gemma":0.001940059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00365503,"about_ca_topic_score_gemma":0.005129218,"domain_scores_codex":[0.9702377,0.02485743,0.0007762599,0.001654063,0.002141204,0.0003333443],"domain_scores_gemma":[0.8218907,0.1532197,0.003333958,0.01783451,0.003203305,0.0005178285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005204891,0.000295231,0.02493047,0.001011488,0.001317553,0.0007266378,0.001027656,0.2275475,0.002128648,0.3254334,0.01287016,0.4021908],"study_design_scores_gemma":[0.00005384664,0.00007108109,0.002732341,0.0001452781,0.0001127042,0.0002768013,0.0001818342,0.6361457,0.001843824,0.3526801,0.005710415,0.00004613432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004219111,0.0001559313,0.9948305,0.00007755065,0.00004193246,0.00004267892,0.00006591839,0.0001924203,0.0003739664],"genre_scores_gemma":[0.2289351,0.0005810176,0.7665189,0.0002574699,0.0001983036,0.000771529,0.0009765232,0.0003679788,0.001393168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04195685,"threshold_uncertainty_score":0.2218917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3189255223302025,"score_gpt":0.446430431089935,"score_spread":0.1275049087597325,"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."}}