{"id":"W2130744536","doi":"","title":"CONFIDENCE INTERVALS FOR PROPORTIONS AND QUANTILES UNDER TWO-STAGE SAMPLING DESIGNS: AN EMPIRICAL STUDY","year":2008,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Quantile; Confidence interval; Statistics; Sampling design; Sampling (signal processing); Independent and identically distributed random variables; National Health and Nutrition Examination Survey; Sample size determination; CDF-based nonparametric confidence interval; Mathematics; Multistage sampling; Population; Stratified sampling; Robust confidence intervals; Coverage probability; Econometrics; Sample (material); Computer science; Demography; Random variable","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":[0.1167715,0.0008349252,0.0015651,0.002784966,0.0006029365,0.002994733,0.003358024,0.002687167,0.004045252],"category_scores_gemma":[0.5373752,0.0005977144,0.001695205,0.005998911,0.00334826,0.004966089,0.002335604,0.003419459,0.0003609624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001622127,"about_ca_system_score_gemma":0.001467334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00260321,"about_ca_topic_score_gemma":0.0008808933,"domain_scores_codex":[0.932343,0.05267482,0.001853535,0.002922004,0.009296305,0.0009103087],"domain_scores_gemma":[0.2288674,0.7326877,0.01350341,0.01377663,0.01026306,0.0009018551],"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":[0.002509414,0.0006888268,0.07445763,0.001893427,0.0008678624,0.0008001004,0.004430995,0.2535506,0.002190189,0.388253,0.003210526,0.2671474],"study_design_scores_gemma":[0.0005710499,0.001986676,0.05072219,0.0009644633,0.0004517424,0.001493284,0.001240559,0.7117254,0.00258945,0.2214456,0.006519686,0.0002898469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2263673,0.007996647,0.7585243,0.0008452596,0.0000861951,0.0004112009,0.0004640698,0.0002821763,0.005022835],"genre_scores_gemma":[0.8189682,0.002404693,0.1769121,0.0001211599,0.000111882,0.00032081,0.000439197,0.00009283895,0.0006290823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1167715,"threshold_uncertainty_score":0.6175541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5587614730044075,"score_gpt":0.5481278381675964,"score_spread":0.01063363483681112,"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."}}