{"id":"W2471313399","doi":"","title":"Multivariate Regression and Variable Selection : A Redundancy Approach","year":2005,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multivariate statistics; Bayesian multivariate linear regression; Feature selection; Selection (genetic algorithm); Redundancy (engineering); Regression; Statistics; Computer science; Multivariate analysis; Regression analysis; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00495824,0.0003709054,0.0005149034,0.000128251,0.0003743654,0.0002131659,0.0004395434,0.0003880295,0.00007514645],"category_scores_gemma":[0.005178159,0.0003446383,0.00009942739,0.0002276762,0.0001570967,0.0001327905,0.000936995,0.0008461023,0.000003968646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118293,"about_ca_system_score_gemma":0.0001624634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003154246,"about_ca_topic_score_gemma":0.0000873808,"domain_scores_codex":[0.9934385,0.004425329,0.0005557148,0.0008910246,0.0003309625,0.0003584415],"domain_scores_gemma":[0.9942557,0.002740806,0.0005299159,0.00112886,0.00114442,0.0002002656],"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":[0.00002104836,0.0005351271,0.00004292794,0.0004343017,0.00005604083,0.000001097224,0.003540977,0.0001225661,0.002308589,0.9042924,0.0004458708,0.08819904],"study_design_scores_gemma":[0.0005938878,5.145861e-7,0.0001864541,0.002075349,0.00009998532,0.00001850695,0.00006004741,0.2162219,0.005392407,0.7706137,0.004248094,0.0004892522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003197195,0.0004547856,0.9538044,0.00101003,0.0001002787,0.000534661,0.00005777627,0.0002400261,0.04060081],"genre_scores_gemma":[0.03602535,0.0002453341,0.9529608,0.00003146459,0.00004366474,0.0001142438,0.0001155824,0.00006433222,0.01039916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2160993,"threshold_uncertainty_score":0.9999006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05731796773485471,"score_gpt":0.3419984926780793,"score_spread":0.2846805249432246,"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."}}