{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002001195,0.0001748348,0.0004222628,0.0001989,0.0002342338,0.00008911065,0.0002179,0.00009802125,0.0002806201],"category_scores_gemma":[0.01890592,0.0001540841,0.00009001572,0.0001695742,0.00008927217,0.0001544595,0.00001193481,0.0002783746,0.000008972393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000152174,"about_ca_system_score_gemma":0.000977783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001858886,"about_ca_topic_score_gemma":0.0005738589,"domain_scores_codex":[0.9980297,0.0001878981,0.0007774146,0.0001124124,0.0002281997,0.0006643931],"domain_scores_gemma":[0.9909123,0.006705769,0.0004626483,0.0001683878,0.0005644352,0.001186415],"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":[0.000009138039,0.00004439436,0.0008368463,0.0001549071,0.00006007527,0.00001906637,0.001016909,0.000007385543,0.00002049522,0.8613156,0.02496794,0.1115472],"study_design_scores_gemma":[0.0003727322,0.0001525994,0.0003802328,0.00009078463,0.0002069613,0.0001131809,0.0002100909,0.05138614,0.00005779361,0.9443644,0.002451666,0.0002133809],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008190353,0.00008150939,0.9970447,0.0003285607,0.0008306372,0.0001984736,0.0009717244,0.000009204497,0.0004532927],"genre_scores_gemma":[0.007006278,0.000001545141,0.9920776,0.0002179245,0.0006033064,0.00001120319,0.00001033702,0.00003713647,0.00003470179],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1113338,"threshold_uncertainty_score":0.9893582,"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."}}