{"id":"W2891328715","doi":"10.1111/insr.12291","title":"Some Theoretical and Practical Aspects of Empirical Likelihood Methods for Complex Surveys","year":2018,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Sample (material); Sample size determination; Empirical likelihood; Sampling (signal processing); Point estimation; Survey sampling; Model selection; Statistical inference; Population; Mathematics; Statistics; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004335605,0.0002059272,0.0006923792,0.00004841616,0.00006362503,0.00003896188,0.0001947566,0.00008841589,0.002622084],"category_scores_gemma":[0.05717134,0.0001561325,0.00009494415,0.0001095839,0.001074369,0.000084179,0.0001417542,0.0001843959,0.0000243944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003828437,"about_ca_system_score_gemma":0.0001070916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004824942,"about_ca_topic_score_gemma":0.000002262232,"domain_scores_codex":[0.9967709,0.001303,0.0008271329,0.0003980991,0.0003902152,0.0003106194],"domain_scores_gemma":[0.9741808,0.02455458,0.0002062626,0.0002265069,0.0005952207,0.000236601],"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":[0.00002855566,0.0001387454,0.00003157202,0.0005879875,0.00006900307,0.000005406466,0.00001236152,6.17249e-10,0.00008211027,0.8062631,0.005711196,0.1870699],"study_design_scores_gemma":[0.0003123762,0.0003727365,0.001790252,0.000522699,0.000164485,0.00005646696,0.000004207975,0.002987382,0.0001602671,0.9796567,0.01379599,0.0001764532],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001715483,0.0007985824,0.9882132,0.004963177,0.0002487149,0.000499503,0.0004418445,0.00002857508,0.004789225],"genre_scores_gemma":[0.006081765,0.001522497,0.9905033,0.001452483,0.0003096462,0.00006055372,0.00003314688,0.0000248345,0.00001182944],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1868935,"threshold_uncertainty_score":0.9982896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1937269300936042,"score_gpt":0.5690216844931857,"score_spread":0.3752947543995815,"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."}}