{"id":"W1971907399","doi":"10.1198/jasa.2010.tm09534","title":"Pseudo–Empirical Likelihood Inference for Multiple Frame Surveys","year":2010,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Empirical likelihood; Inference; Point estimation; Statistics; Confidence interval; Likelihood function; Mathematics; Statistic; Population; Interval estimation; Confidence distribution; Econometrics; Statistical inference; Frame (networking); Expectation–maximization algorithm; Computer science; Estimation theory; Maximum likelihood; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0262823,0.001041195,0.001625543,0.00255834,0.0008702007,0.002568455,0.003604266,0.001743899,0.004098814],"category_scores_gemma":[0.1731783,0.00112776,0.001695769,0.002765946,0.002712687,0.005837174,0.003316964,0.003190743,0.0008539452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001482804,"about_ca_system_score_gemma":0.001715924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00217461,"about_ca_topic_score_gemma":0.001610794,"domain_scores_codex":[0.9806136,0.01467819,0.0005663948,0.001661504,0.002191055,0.0002892741],"domain_scores_gemma":[0.9076459,0.07602982,0.004473296,0.007939938,0.00348185,0.0004291131],"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":[0.0001179005,0.00005326097,0.002970555,0.000197396,0.0001497543,0.0002040201,0.0003657671,0.07071605,0.0004441988,0.831741,0.001771446,0.09126871],"study_design_scores_gemma":[0.00005232963,0.00006370711,0.001197588,0.00007776226,0.00003735346,0.0001793601,0.00007259155,0.4731166,0.0005884013,0.5201866,0.004386427,0.00004141048],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001793513,0.00009730435,0.9974924,0.0000797659,0.00001738257,0.00002230583,0.00002685907,0.00005614357,0.000414236],"genre_scores_gemma":[0.1871061,0.0006100407,0.8087869,0.0002499032,0.0002152983,0.0007165423,0.0004513697,0.0001144944,0.001749311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0262823,"threshold_uncertainty_score":0.1389958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04316731173248054,"score_gpt":0.4051995834760186,"score_spread":0.362032271743538,"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."}}