{"id":"W2294933899","doi":"10.1002/cjs.11275","title":"Jackknife empirical likelihood for comparing two Gini indices","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Georgia State University","keywords":"Jackknife resampling; Empirical likelihood; Statistics; Mathematics; Econometrics; Nuisance parameter; Statistic; Maximization; Missing data; Confidence interval; Estimator; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007042415,0.0001560298,0.0004387228,0.0001984293,0.0001346906,0.00007786131,0.0002859574,0.00006613348,0.000343245],"category_scores_gemma":[0.005739655,0.0001062272,0.00007292747,0.0001033686,0.0001814249,0.00009598351,0.00001203646,0.0001695399,0.00001479702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001481632,"about_ca_system_score_gemma":0.001141808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002354644,"about_ca_topic_score_gemma":0.006256025,"domain_scores_codex":[0.9984269,0.00009391712,0.00066018,0.0001354716,0.0002115207,0.000472007],"domain_scores_gemma":[0.9942726,0.003820284,0.0003960546,0.0001534511,0.0004639543,0.0008936758],"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":[0.00003646473,0.00002959626,0.02089572,0.00009948953,0.00008567161,0.0001732386,0.00039213,3.318288e-7,0.00007406674,0.7796468,0.06145708,0.1371094],"study_design_scores_gemma":[0.001081437,0.0002706513,0.004864881,0.0002351366,0.00009938643,0.00009480344,0.00008349612,0.0001950117,0.0001332326,0.9831387,0.009608181,0.0001950134],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005815855,0.00005450339,0.9912126,0.0006798754,0.0004800455,0.0001214199,0.0008998064,0.000006971619,0.0007289202],"genre_scores_gemma":[0.2288006,0.000007564654,0.7706856,0.0001532532,0.0002677695,0.000003256829,0.000001817608,0.00002330401,0.0000569508],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2229847,"threshold_uncertainty_score":0.6871318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1271092490982263,"score_gpt":0.394174626489652,"score_spread":0.2670653773914257,"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."}}