{"id":"W3141775592","doi":"10.1007/s10463-021-00793-4","title":"Empirical likelihood meta-analysis with publication bias correction under Copas-like selection model","year":2021,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Restricted maximum likelihood; Statistics; Mathematics; Likelihood principle; Marginal likelihood; Empirical likelihood; Estimator; Likelihood function; Maximum likelihood sequence estimation; Likelihood-ratio test; Maximum likelihood; Inference; Conditional probability distribution; Econometrics; Model selection; Parametric statistics; Selection (genetic algorithm); Quasi-maximum likelihood; Computer science; 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":[0.0009827773,0.0002852087,0.001293598,0.0001585735,0.000138484,0.00006595,0.0003095583,0.0001444073,0.0003288254],"category_scores_gemma":[0.005226237,0.0001761305,0.0005332385,0.00153865,0.0004126177,0.0001944188,0.0001152011,0.0002679877,0.000005480968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002969739,"about_ca_system_score_gemma":0.0003650284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003729512,"about_ca_topic_score_gemma":0.0001524355,"domain_scores_codex":[0.9970475,0.0002694144,0.001142885,0.0003735399,0.0008591483,0.0003075244],"domain_scores_gemma":[0.9943476,0.00230753,0.0007947835,0.0006990032,0.001705979,0.0001450758],"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":[0.00003981958,0.0009492113,0.0001258351,0.0003732834,0.01370697,0.000002989469,0.0002180086,0.001434744,0.0001997677,0.9759285,0.006215811,0.0008049997],"study_design_scores_gemma":[0.0001670272,0.0001043357,0.000389505,0.00004015702,0.02992424,0.00002444206,0.00008096955,0.1007582,0.005468,0.8627541,0.00007585403,0.00021323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00246148,0.00003666439,0.9928814,0.001915895,0.0001315026,0.0002664492,0.0002796255,0.00004016645,0.001986772],"genre_scores_gemma":[0.180411,0.00001445454,0.8187252,0.0003984083,0.0000180928,0.00003366187,0.0000258539,0.00002531805,0.0003480875],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1779495,"threshold_uncertainty_score":0.7182395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3490179428316027,"score_gpt":0.4380967730010622,"score_spread":0.0890788301694595,"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."}}