{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005879144,0.000118699,0.0003238894,0.00007183909,0.00008571172,0.00004595586,0.00006968522,0.00007190122,0.00006409275],"category_scores_gemma":[0.004605101,0.00008960439,0.00002529092,0.0001073417,0.00005089377,0.0001334734,0.000009218019,0.0003700431,0.000001029911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002584666,"about_ca_system_score_gemma":0.0001078611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000123794,"about_ca_topic_score_gemma":1.37647e-7,"domain_scores_codex":[0.9988467,0.000106219,0.0004529918,0.0001269136,0.000243001,0.0002241498],"domain_scores_gemma":[0.9962301,0.003095731,0.0001969511,0.00005898516,0.0001999476,0.0002182855],"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":[0.0003647291,0.0003691177,0.002942879,0.00009644335,0.00003504051,0.0001137006,0.0002611309,0.0006977329,0.001359681,0.9090949,0.005948727,0.07871588],"study_design_scores_gemma":[0.00043397,0.0009893286,0.004941099,0.0001567132,0.00005027524,0.00006185282,0.00002575586,0.03140761,0.00009374603,0.9611855,0.0005337929,0.0001203506],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004117337,0.00005824406,0.9944339,0.0002125688,0.00006099899,0.00004018838,0.00001837399,0.00001705124,0.001041287],"genre_scores_gemma":[0.3566689,0.000003489306,0.6429984,0.0002523931,0.00005715482,4.260288e-7,9.191425e-7,0.000004467383,0.00001389986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3525515,"threshold_uncertainty_score":0.5513069,"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."}}