{"id":"W2068394532","doi":"10.1002/cjs.10138","title":"Approximate jackknife empirical likelihood method for estimating equations","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Security Agency; National Science Foundation","keywords":"Jackknife resampling; Empirical likelihood; Estimator; Estimating equations; Nuisance; Mathematics; Statistics; Nuisance parameter; Econometrics; Computation; Algorithm","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":[0.01884957,0.001425371,0.003248078,0.002672579,0.001141866,0.002574204,0.005405331,0.002453492,0.007516309],"category_scores_gemma":[0.1081805,0.001413586,0.001931867,0.003411245,0.002410897,0.004135932,0.002393535,0.004485903,0.0023667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001957326,"about_ca_system_score_gemma":0.003294684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01047954,"about_ca_topic_score_gemma":0.009196792,"domain_scores_codex":[0.980234,0.01514552,0.0005618762,0.001496595,0.002180776,0.0003813119],"domain_scores_gemma":[0.9548897,0.03610145,0.002073125,0.003253958,0.003303129,0.0003786725],"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":[0.0002778163,0.0001157149,0.00391226,0.0005595789,0.0003970407,0.0005587665,0.0007328115,0.3313736,0.001200665,0.4548866,0.006563113,0.1994221],"study_design_scores_gemma":[0.00003484856,0.00003561752,0.0005134844,0.0001089059,0.00004217376,0.0001744892,0.0000715918,0.8398812,0.0005692469,0.1524171,0.006105693,0.0000455581],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006542702,0.00009565044,0.9987488,0.00004691257,0.00001188261,0.00002957658,0.00002793146,0.00006920069,0.0003157095],"genre_scores_gemma":[0.06798916,0.0004624591,0.9267575,0.0001546629,0.00007772172,0.0005960384,0.0004011243,0.0002030572,0.00335817],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01884957,"threshold_uncertainty_score":0.09968722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1394659831182003,"score_gpt":0.421718548225866,"score_spread":0.2822525651076657,"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."}}