{"id":"W2142707184","doi":"10.1002/cjs.11200","title":"Replication variance estimation in unequal probability sampling without replacement: One‐stage and two‐stage","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Acadia University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster sampling; Jackknife resampling; Replication (statistics); Statistics; Sampling (signal processing); Sampling design; Stratified sampling; Variance (accounting); Poisson sampling; Sample (material); Stage (stratigraphy); Multistage sampling; Mathematics; Sample size determination; Fraction (chemistry); Population; Econometrics; Importance sampling; Slice sampling; Computer science; Estimator; Monte Carlo method; Biology; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.09218328,0.001400257,0.002928543,0.002121728,0.001325973,0.002017901,0.004462513,0.002326955,0.00375278],"category_scores_gemma":[0.3158058,0.00118614,0.002898383,0.003917217,0.003315086,0.002434757,0.003241363,0.002620901,0.001248878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00175307,"about_ca_system_score_gemma":0.0042053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004014083,"about_ca_topic_score_gemma":0.003000973,"domain_scores_codex":[0.8463222,0.1302533,0.003958402,0.009092614,0.009214932,0.00115848],"domain_scores_gemma":[0.8088495,0.1212222,0.01219485,0.04199292,0.01496853,0.0007720307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001310897,0.000481363,0.02388611,0.001815298,0.002171268,0.0008707916,0.003109276,0.06381946,0.005767169,0.3365032,0.007340414,0.5529246],"study_design_scores_gemma":[0.0006286509,0.001298673,0.01866605,0.0008100309,0.001077253,0.0009453202,0.0006266652,0.5457918,0.01059665,0.4011184,0.01809554,0.0003450729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007633511,0.0002552715,0.9905843,0.0001277686,0.00008285389,0.0005536221,0.00007663909,0.0001304451,0.0005555974],"genre_scores_gemma":[0.1881557,0.0003003982,0.8067202,0.0001397685,0.00008448333,0.002577139,0.0002899594,0.0001209634,0.001611418],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09218328,"threshold_uncertainty_score":0.4875177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1313305944076682,"score_gpt":0.3534591225779179,"score_spread":0.2221285281702497,"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."}}