{"id":"W3042907151","doi":"10.1007/s42081-020-00084-x","title":"Empirical likelihood and estimating equations for survey data analysis","year":2020,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point estimation; Statistics; Computer science; Population; Bayesian probability; Mathematics; Econometrics; Statistical inference; Statistical hypothesis testing; Empirical likelihood; Confidence interval; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0280734,0.001579392,0.003025637,0.003715354,0.0009501794,0.003374044,0.004248486,0.002576239,0.005333979],"category_scores_gemma":[0.1533513,0.001981157,0.002963656,0.005814987,0.003411818,0.006046788,0.003698252,0.006225415,0.001315672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002306668,"about_ca_system_score_gemma":0.00401641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007780139,"about_ca_topic_score_gemma":0.004198556,"domain_scores_codex":[0.9789216,0.0167121,0.001096956,0.001758593,0.001249056,0.0002618148],"domain_scores_gemma":[0.845294,0.1431673,0.002959582,0.005452703,0.002741411,0.0003849874],"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":[0.00003458593,0.00004537866,0.002912691,0.0003676038,0.0002503913,0.0001614178,0.0004656396,0.0530818,0.0002092728,0.8790569,0.004302085,0.05911225],"study_design_scores_gemma":[0.00002034573,0.00001433265,0.0007942125,0.0000672892,0.00008205069,0.0001290386,0.00006092015,0.1940506,0.0001075711,0.7991706,0.005469924,0.00003307373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001511679,0.0008968629,0.9962627,0.0005006334,0.00003859311,0.00003188087,0.0002052637,0.0001094888,0.0004429852],"genre_scores_gemma":[0.1165109,0.006121821,0.8642263,0.0005607504,0.0007670231,0.00156942,0.002361622,0.0005120656,0.007370202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0280734,"threshold_uncertainty_score":0.1484681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3675017885248815,"score_gpt":0.4945654689663485,"score_spread":0.127063680441467,"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."}}