{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01646133,0.0008678979,0.001537739,0.00228449,0.000572534,0.0018096,0.003639217,0.001644781,0.006429159],"category_scores_gemma":[0.1304376,0.0006026932,0.001159288,0.001726963,0.002098148,0.00268406,0.002112759,0.002881815,0.00132775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00159451,"about_ca_system_score_gemma":0.001337099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00420193,"about_ca_topic_score_gemma":0.003222215,"domain_scores_codex":[0.9853619,0.01037006,0.0004731092,0.001572162,0.001868639,0.0003540824],"domain_scores_gemma":[0.9365216,0.0436748,0.003378565,0.01080636,0.005210972,0.0004076355],"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":[0.0004864674,0.0001534569,0.006350879,0.0004506605,0.0003535814,0.0005926474,0.0004260199,0.3243192,0.002868947,0.3801782,0.01313933,0.2706806],"study_design_scores_gemma":[0.00005942938,0.00009359644,0.003692182,0.00009141904,0.00005644089,0.0002261155,0.00006766845,0.8312266,0.002243145,0.1536954,0.008458924,0.0000891207],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00693347,0.0002990136,0.9897354,0.0001896785,0.0001004569,0.00008257709,0.0001418704,0.0004112115,0.002106384],"genre_scores_gemma":[0.381792,0.0003694651,0.6112339,0.0005271773,0.0003535954,0.0003958052,0.001034573,0.0004006362,0.003892855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01646133,"threshold_uncertainty_score":0.08705688,"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."}}