{"id":"W1490051656","doi":"10.1016/j.jmva.2010.05.007","title":"Conditional information criteria for selecting variables in linear mixed models","year":2010,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Akaike information criterion; Mathematics; Estimator; Bayesian information criterion; Conditional variance; Conditional expectation; Marginal model; Context (archaeology); Random effects model; Dimension (graph theory); Linear model; Statistics; Generalized linear mixed model; Model selection; Information Criteria; Conditional probability distribution; Econometrics; Regression analysis; Combinatorics","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":[0.001485164,0.000105857,0.0003061235,0.0006992634,0.0000877006,0.0001819139,0.0004379456,0.00009887455,0.00001765244],"category_scores_gemma":[0.0003083272,0.00009145415,0.0002112242,0.0008771535,0.00001438311,0.002151164,0.00004408145,0.0003149091,0.000001735947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002927369,"about_ca_system_score_gemma":0.000147124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008526499,"about_ca_topic_score_gemma":0.00004216842,"domain_scores_codex":[0.9986737,0.00006442304,0.0007008338,0.0001204033,0.0002537851,0.0001868491],"domain_scores_gemma":[0.9982775,0.0002427117,0.000499846,0.0001670032,0.0007290235,0.00008386358],"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":[0.0001140599,0.0002808768,0.00147093,0.00003816874,0.001205822,0.000008087276,0.003276542,0.8370281,0.03243006,0.1123396,0.0003699377,0.01143787],"study_design_scores_gemma":[0.0005366324,0.00004012748,0.001599359,0.00001256251,0.0001254499,0.00001214087,0.00003026548,0.9643985,0.000694265,0.03231186,0.000135108,0.0001037083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0560098,0.000007029746,0.9432047,0.0003325466,0.0002957581,0.00004944016,0.000007977956,0.00001422666,0.00007849772],"genre_scores_gemma":[0.6688418,0.000002875903,0.3309757,0.00008066825,0.00007546524,0.000003043859,0.000009472599,0.000002360011,0.000008574093],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.612832,"threshold_uncertainty_score":0.3729392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02982968253782834,"score_gpt":0.3125854939293415,"score_spread":0.2827558113915132,"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."}}