{"id":"W2090131474","doi":"10.1016/j.jspi.2009.09.025","title":"Empirical likelihood based variable selection","year":2009,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of British Columbia; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Empirical likelihood; Mathematics; Parametric statistics; Likelihood function; Model selection; Parametric model; Selection (genetic algorithm); Set (abstract data type); Information Criteria; Variable (mathematics); Mathematical optimization; Feature selection; Constraint (computer-aided design); Likelihood principle; Applied mathematics; Maximum likelihood; Statistics; Computer science; Quasi-maximum likelihood; Artificial intelligence; Estimator","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007794858,0.0008481947,0.002116095,0.002266384,0.00097693,0.002281005,0.002403842,0.001640533,0.009986606],"category_scores_gemma":[0.03541391,0.001226428,0.001601867,0.002338557,0.001494745,0.002459648,0.002534307,0.003115425,0.002254863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000722822,"about_ca_system_score_gemma":0.002117677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001846822,"about_ca_topic_score_gemma":0.002181866,"domain_scores_codex":[0.9951703,0.003431443,0.000159675,0.0004916249,0.0005876062,0.0001593762],"domain_scores_gemma":[0.9760218,0.02011828,0.0005502505,0.001675192,0.001374079,0.0002603352],"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":[0.0005768759,0.0002676024,0.004535237,0.0002651992,0.0004599339,0.0002977649,0.0001642542,0.252625,0.001501797,0.2269349,0.01703663,0.4953349],"study_design_scores_gemma":[0.00009893295,0.00004619596,0.0005391166,0.00003399115,0.0000520859,0.0000849178,0.00001504344,0.8793744,0.0007836439,0.1156686,0.003283624,0.00001952096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003900294,0.0003560509,0.99335,0.0004123859,0.00007308941,0.00004514664,0.00009391447,0.0005433718,0.001225789],"genre_scores_gemma":[0.2292745,0.0007105883,0.757561,0.0005104836,0.0004248382,0.0004595704,0.001143017,0.0006248938,0.009291102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009986606,"threshold_uncertainty_score":0.04122365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1239338934358652,"score_gpt":0.4624732647641817,"score_spread":0.3385393713283165,"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."}}