{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0382531,0.0009073396,0.002660205,0.005440155,0.001125696,0.003221444,0.003571501,0.002729558,0.003983571],"category_scores_gemma":[0.2615551,0.0007661683,0.001435953,0.003577043,0.004624858,0.004636729,0.003297148,0.003754445,0.0009883365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830682,"about_ca_system_score_gemma":0.001433428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001617802,"about_ca_topic_score_gemma":0.0009424009,"domain_scores_codex":[0.9770156,0.01614335,0.0009288752,0.00256699,0.00287956,0.000465586],"domain_scores_gemma":[0.8540579,0.1281599,0.005491938,0.007150866,0.004171267,0.0009682399],"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":[0.0007413191,0.0001651235,0.03487969,0.0008011608,0.0008945014,0.0006400451,0.00169408,0.2433948,0.002107547,0.4881533,0.00860515,0.2179233],"study_design_scores_gemma":[0.00005385744,0.0001240677,0.005645183,0.0002184908,0.00006579278,0.0004929572,0.0003327271,0.5320457,0.001951028,0.4539413,0.005030225,0.00009867572],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02083202,0.0004357504,0.9760059,0.0002203273,0.00004526572,0.0001121665,0.0002333652,0.000244111,0.001870991],"genre_scores_gemma":[0.5422627,0.0004747429,0.452205,0.0003184329,0.0001455553,0.001031753,0.001525683,0.000443841,0.001592134],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0382531,"threshold_uncertainty_score":0.2023041,"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."}}