{"id":"W1916909999","doi":"10.1002/cjs.11187","title":"A new replicate variance estimator for unequal probability sampling without replacement","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":"","funders":"","keywords":"Replicate; Estimator; Population variance; Statistics; Variance (accounting); Mathematics; Sampling (signal processing); Consistency (knowledge bases); Sampling design; Efficient estimator; Bias of an estimator; Consistent estimator; Minimum-variance unbiased estimator; Population; Econometrics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01876514,0.0006487438,0.001814484,0.002185653,0.0007131202,0.001664881,0.004342572,0.002323629,0.003128656],"category_scores_gemma":[0.0964402,0.0007588107,0.001752609,0.001967763,0.001658256,0.002401131,0.002555007,0.002205973,0.0008312791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105638,"about_ca_system_score_gemma":0.001416424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001566631,"about_ca_topic_score_gemma":0.001340722,"domain_scores_codex":[0.9824688,0.01105466,0.0006685969,0.002239197,0.003241995,0.0003266172],"domain_scores_gemma":[0.9527655,0.0300882,0.002728491,0.008623568,0.005349868,0.0004442802],"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":[0.0004832888,0.0003564623,0.02798202,0.0004550065,0.00114226,0.0003391216,0.0006640723,0.1419602,0.0118439,0.3105226,0.006443062,0.4978079],"study_design_scores_gemma":[0.0001761872,0.0004209706,0.006327727,0.0001227581,0.0002868703,0.000517059,0.00009961237,0.8410585,0.008404942,0.1318772,0.01058565,0.000122516],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004335176,0.0000794392,0.9950028,0.00005132861,0.00002872952,0.00004487909,0.0000447551,0.0001161194,0.0002967256],"genre_scores_gemma":[0.226629,0.0001768144,0.7689199,0.000255074,0.0001929156,0.0007221363,0.0005171535,0.0002181014,0.002368843],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01876514,"threshold_uncertainty_score":0.09924072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1631710627063437,"score_gpt":0.3555048550044894,"score_spread":0.1923337922981457,"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."}}