{"id":"W4319841290","doi":"10.1002/cjs.11756","title":"Regression model selection via log‐likelihood ratio and constrained minimum criterion","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Akaike information criterion; Bayesian information criterion; Likelihood-ratio test; Statistics; Deviance information criterion; Model selection; Mathematics; Frequentist inference; Information Criteria; Likelihood principle; Sample size determination; Score test; Regression analysis; Selection (genetic algorithm); Ratio test; Bayesian probability; Likelihood function; Maximum likelihood; Bayesian inference; Computer science; Artificial intelligence; Quasi-maximum likelihood","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":[0.01610658,0.001628272,0.002615079,0.004048531,0.0008474148,0.001947676,0.003409019,0.001768874,0.004265368],"category_scores_gemma":[0.09269112,0.0008144993,0.001714459,0.002673951,0.001700516,0.002049036,0.002124953,0.002515691,0.0008747673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001508225,"about_ca_system_score_gemma":0.002956069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005928419,"about_ca_topic_score_gemma":0.003632924,"domain_scores_codex":[0.9836902,0.01327966,0.000366152,0.0007571451,0.001643502,0.0002634229],"domain_scores_gemma":[0.9342611,0.05887281,0.002416378,0.001322394,0.002673822,0.0004535473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003458464,0.0002225849,0.004326706,0.0004257693,0.0004392114,0.0004929825,0.0001569816,0.7728884,0.001584932,0.09330279,0.004626536,0.1211873],"study_design_scores_gemma":[0.00003257072,0.00004521963,0.0003321032,0.00002483327,0.00001592979,0.00004508227,0.00001163668,0.97516,0.0002962078,0.02360211,0.0004177609,0.00001659837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008045462,0.0002796447,0.9901791,0.0002485566,0.00002142462,0.00008600346,0.00007077988,0.0003562651,0.000712703],"genre_scores_gemma":[0.3285936,0.0003638693,0.6680859,0.0002593217,0.00008031538,0.0005800498,0.0004545964,0.0003825207,0.001199848],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01610658,"threshold_uncertainty_score":0.08518076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05334615844499688,"score_gpt":0.3330438830271936,"score_spread":0.2796977245821967,"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."}}