{"id":"W2061973614","doi":"10.1002/cjs.11234","title":"Resampling calibrated adjusted empirical likelihood","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Killam Trusts; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Empirical likelihood; Resampling; Confidence region; Inference; Statistics; Dimension (graph theory); Statistical inference; Confidence interval; Sample (material); Set (abstract data type); Mathematics; Coverage probability; Sample size determination; Econometrics; Computer science; Artificial intelligence","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":[0.0008641098,0.0001501197,0.0003837376,0.0001909913,0.0001251347,0.00008549398,0.0002489813,0.0001033987,0.000540191],"category_scores_gemma":[0.01332953,0.0001291754,0.00005060185,0.0002142765,0.0001321663,0.00005688968,0.00001140952,0.0003891141,0.00001839965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009794857,"about_ca_system_score_gemma":0.001020096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008402685,"about_ca_topic_score_gemma":0.004937939,"domain_scores_codex":[0.9982862,0.0002417408,0.0006860681,0.0001230402,0.0002398766,0.0004230836],"domain_scores_gemma":[0.9953132,0.002549265,0.0003151437,0.0001884162,0.000561531,0.001072487],"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":[0.00003095178,0.00004133884,0.008837257,0.0001273935,0.00008505837,0.0003226399,0.0007731887,0.00002175223,0.00008008242,0.7886029,0.1093913,0.09168611],"study_design_scores_gemma":[0.0005621081,0.0003823073,0.006491525,0.0001591079,0.0001122797,0.0001419169,0.0001386735,0.006752557,0.0001115995,0.9629577,0.02192702,0.0002632097],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01340284,0.00004738133,0.9829621,0.0003534324,0.0004795363,0.00006167279,0.0002800529,0.00001062599,0.002402331],"genre_scores_gemma":[0.2683321,0.000005183608,0.73094,0.0003930683,0.0002258719,8.162614e-7,0.000004477108,0.00002817096,0.00007029533],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2549292,"threshold_uncertainty_score":0.9949816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1594629708581123,"score_gpt":0.3562555843509952,"score_spread":0.196792613492883,"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."}}