{"id":"W94208768","doi":"10.1002/jae.2508","title":"Wild Bootstrap Inference for Wildly Different Cluster Sizes","year":2016,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":373,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Queen's University","funders":"","keywords":"Estimator; Cluster (spacecraft); Econometrics; Statistics; Variance (accounting); Monte Carlo method; Inference; Mathematics; Computer science; Economics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000830597,0.0002029041,0.000611045,0.0004664866,0.00006066844,0.00007101208,0.0003396762,0.0001179354,0.0002448556],"category_scores_gemma":[0.003155097,0.0001186596,0.0001819299,0.0002750047,0.00008345532,0.0001428172,0.00005394469,0.0001631488,0.00001233096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113366,"about_ca_system_score_gemma":0.00006310038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.184344e-7,"about_ca_topic_score_gemma":0.000001362569,"domain_scores_codex":[0.9983181,0.00002671744,0.0009252542,0.0001985157,0.0002134744,0.0003179833],"domain_scores_gemma":[0.9894807,0.009143678,0.0007636725,0.0002239162,0.0001798451,0.0002082375],"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":[0.0002156735,0.0001810501,0.001150406,0.0001079195,0.00009987585,0.000002142661,0.00007514793,0.000001324291,0.0003898415,0.7946407,0.003372194,0.1997637],"study_design_scores_gemma":[0.00156188,0.0003448732,0.002852589,0.00007394233,0.00007763135,0.00000974334,0.00004197139,0.00007349286,0.00137278,0.9904428,0.002933462,0.0002148479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03860481,0.0000346295,0.957531,0.0005224494,0.0003213786,0.0002421376,0.00005301482,0.00001470313,0.002675882],"genre_scores_gemma":[0.5872669,0.0001039986,0.4120505,0.0001793362,0.0002780029,0.00001660092,4.079386e-7,0.00002462845,0.00007963023],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5486621,"threshold_uncertainty_score":0.4838798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1001847994653385,"score_gpt":0.3560613191813742,"score_spread":0.2558765197160357,"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."}}