{"id":"W2319671594","doi":"10.4310/sii.2012.v5.n3.a7","title":"Empirical likelihood inference for two-sample problems","year":2012,"lang":"en","type":"article","venue":"Statistics and Its Interface","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Empirical likelihood; Inference; Econometrics; Statistics; Mathematics; Sample (material); Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0003962738,0.000176689,0.0002733542,0.00002894236,0.0001062519,0.00003516101,0.00009349058,0.00005534288,0.00008025662],"category_scores_gemma":[0.004407499,0.0001461381,0.00002541755,0.00004831381,0.00005220892,0.0001164235,0.00008735769,0.000139748,0.000008675394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002262082,"about_ca_system_score_gemma":0.00002417756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006387907,"about_ca_topic_score_gemma":0.000009519403,"domain_scores_codex":[0.9988158,0.00005606332,0.0003103174,0.0002202439,0.0001295192,0.0004680014],"domain_scores_gemma":[0.9951062,0.004271739,0.00009677732,0.0001449416,0.0001534896,0.00022686],"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":[0.00003320041,0.0001490369,0.0001595844,0.0002912064,0.00003054199,4.042499e-7,0.001516804,0.00001862681,0.0005779798,0.9651363,0.0032535,0.02883279],"study_design_scores_gemma":[0.0004934102,0.0002302718,0.0000552596,0.00005106222,0.00005114281,0.000003481711,0.00008525401,0.02441414,0.001215439,0.9622573,0.0109014,0.0002418802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003400962,0.0003442402,0.9934883,0.00008219012,0.0002276102,0.000401227,0.001690674,0.00003959265,0.000325212],"genre_scores_gemma":[0.3255901,0.00002602233,0.6739303,0.00006758919,0.00007460512,0.00004550136,0.00001104814,0.00002369975,0.0002311564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3221891,"threshold_uncertainty_score":0.5959341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1795196011590122,"score_gpt":0.4855336518472913,"score_spread":0.3060140506882791,"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."}}