{"id":"W2009378006","doi":"10.2307/3315996","title":"Combining information from multiple surveys through the empirical likelihood method","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Consistency (knowledge bases); Construct (python library); Computer science; Empirical likelihood; Maximum likelihood; Mathematics; Statistics; Quasi-maximum likelihood; Econometrics; Likelihood function; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"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":[0.02273992,0.001144156,0.002705079,0.005092232,0.0004864114,0.003265411,0.002480195,0.001820153,0.003227443],"category_scores_gemma":[0.08095817,0.001495534,0.001541163,0.006340177,0.001815377,0.005637047,0.004227408,0.002105099,0.0006855439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140868,"about_ca_system_score_gemma":0.001615904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001984558,"about_ca_topic_score_gemma":0.001544113,"domain_scores_codex":[0.9704023,0.02388783,0.0007308502,0.001429131,0.003204753,0.0003450858],"domain_scores_gemma":[0.9530911,0.03835926,0.00275773,0.003206735,0.002190634,0.0003945066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003107009,0.0001667095,0.01005705,0.0008599404,0.001147989,0.000532373,0.0005538755,0.2909515,0.001285548,0.3355724,0.002678221,0.3558837],"study_design_scores_gemma":[0.00006681458,0.0001345693,0.002481657,0.0001774297,0.0001554575,0.0001773385,0.0001036164,0.6494274,0.0008673948,0.3413438,0.004973729,0.00009086132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00526111,0.0004033415,0.9930131,0.0003114736,0.00002819346,0.0000462127,0.00006542198,0.00009134063,0.0007799024],"genre_scores_gemma":[0.3376546,0.001825896,0.6551737,0.0004387379,0.0003919736,0.000398099,0.0006206239,0.0001270069,0.003369389],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02273992,"threshold_uncertainty_score":0.1202616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.134421380343414,"score_gpt":0.3737763162867002,"score_spread":0.2393549359432862,"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."}}