{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005203731,0.000134306,0.0002755111,0.0002683281,0.0001705893,0.00008097605,0.00009068989,0.00008956502,0.000110957],"category_scores_gemma":[0.00203967,0.0001153712,0.00002850604,0.0002188488,0.0001466666,0.00009187537,0.000009676842,0.0002300578,0.000006216283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007773172,"about_ca_system_score_gemma":0.0007331289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001564483,"about_ca_topic_score_gemma":0.001716277,"domain_scores_codex":[0.9988253,0.0001000109,0.0004680931,0.0001213394,0.0001795944,0.0003057211],"domain_scores_gemma":[0.9980807,0.0007017537,0.0002408736,0.00008218145,0.0003535597,0.0005409754],"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":[0.00005219756,0.00002733953,0.001123383,0.0002874584,0.00007171186,0.0005950372,0.001588413,0.00003944211,0.004521932,0.7178672,0.06526231,0.2085636],"study_design_scores_gemma":[0.0003296532,0.0001709089,0.0009309921,0.000138589,0.00005901585,0.000196317,0.0001644206,0.1544749,0.0001657255,0.8431113,0.0001206041,0.0001376299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0066685,0.00003058068,0.9920418,0.0002905682,0.0002397483,0.00008455823,0.0004182309,0.00001368952,0.000212357],"genre_scores_gemma":[0.2318553,0.00003334064,0.767826,0.00007988239,0.00008719725,0.000001699421,0.000008960886,0.0000195168,0.00008816623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2251868,"threshold_uncertainty_score":0.4704702,"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."}}