{"id":"W1979604055","doi":"10.1002/cjs.11237","title":"Generalized pseudo empirical likelihood inferences for complex surveys","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Empirical likelihood; Statistics; Mathematics; Estimator; Weighting; Statistic; Confidence interval; Calibration; Confidence distribution; Applied mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002333513,0.0001839971,0.0005309226,0.0001911165,0.0001180403,0.0001305128,0.0003205172,0.0001005785,0.0003153485],"category_scores_gemma":[0.01112656,0.0001537949,0.00007705584,0.0001686375,0.0001911927,0.00008000766,0.00001287805,0.0002185662,0.000009544316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001527179,"about_ca_system_score_gemma":0.002980412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001131777,"about_ca_topic_score_gemma":0.0129078,"domain_scores_codex":[0.9978579,0.0004726726,0.0007580682,0.0001408759,0.0002988368,0.0004716922],"domain_scores_gemma":[0.9942093,0.002385633,0.0003809376,0.0001687836,0.00134408,0.001511205],"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":[0.00002825451,0.00004097985,0.008751406,0.00007377809,0.00008073922,0.0001457311,0.0006840415,0.000002616894,0.00001878752,0.6574319,0.2876377,0.04510406],"study_design_scores_gemma":[0.0009821571,0.0004688529,0.006229936,0.00004311699,0.00009169273,0.00009123552,0.0001891434,0.001937744,0.00001978533,0.9796198,0.01011075,0.0002157707],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00328551,0.00005698117,0.9929725,0.0004897648,0.0005969809,0.0001584241,0.001817385,0.000007853119,0.0006146175],"genre_scores_gemma":[0.0514872,0.000006892462,0.9478673,0.0002466259,0.0002741132,0.000004736569,0.00002276461,0.00002645706,0.00006389974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3221879,"threshold_uncertainty_score":0.9972031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2740594724168929,"score_gpt":0.4136428448665517,"score_spread":0.1395833724496587,"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."}}