{"id":"W2164422502","doi":"10.1111/j.1467-9868.2006.00555.x","title":"Adjusted Jackknife for Imputation under Unequal Probability Sampling Without Replacement","year":2006,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Southampton","keywords":"Jackknife resampling; Imputation (statistics); Statistics; Estimator; Variance (accounting); Mathematics; Simple random sample; Missing data; Econometrics; Probability sampling; Sampling (signal processing); Sample size determination; Computer science; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006093225,0.0004861421,0.001310681,0.00003949866,0.000521026,0.00009128054,0.0004593253,0.0003448756,0.0002047926],"category_scores_gemma":[0.02649948,0.0003266803,0.0005036446,0.000226128,0.001025982,0.0001618761,0.0001934026,0.0008818099,0.000002533077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004039881,"about_ca_system_score_gemma":0.0002686147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005745706,"about_ca_topic_score_gemma":0.00003590657,"domain_scores_codex":[0.9935018,0.002078128,0.002148575,0.0005834628,0.0008405982,0.0008473767],"domain_scores_gemma":[0.9677895,0.02945735,0.001144875,0.0004301107,0.0008670593,0.0003111301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001605229,0.0003411749,0.0002038819,0.0004283615,0.000231729,0.000005644032,0.0001745926,0.007338367,0.0003330241,0.9737316,0.007082953,0.008523421],"study_design_scores_gemma":[0.001653378,0.0009690544,0.003899901,0.00007601328,0.0006084835,0.00004421956,0.000340019,0.02015725,0.0002590847,0.9701658,0.001446119,0.0003806163],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00475401,0.0000632253,0.9908956,0.001346621,0.0008511375,0.0009862049,0.0009106636,0.00005530228,0.0001372081],"genre_scores_gemma":[0.02450787,0.000006902997,0.9740827,0.0003543101,0.0004832153,0.00006099214,0.0000310549,0.0000742109,0.0003987322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02737923,"threshold_uncertainty_score":0.9999185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2616683562810396,"score_gpt":0.4481849849324592,"score_spread":0.1865166286514195,"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."}}