{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01409015,0.001763116,0.003035801,0.003708986,0.001149992,0.002344817,0.002874329,0.00188014,0.004190335],"category_scores_gemma":[0.04705595,0.001093929,0.00254088,0.005107084,0.002416707,0.002645484,0.002740431,0.002295172,0.0009847601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008657689,"about_ca_system_score_gemma":0.001679373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001647234,"about_ca_topic_score_gemma":0.001613139,"domain_scores_codex":[0.9868426,0.009449153,0.0004470271,0.001165871,0.001802402,0.0002930307],"domain_scores_gemma":[0.9511828,0.0381763,0.002599045,0.004753945,0.002869551,0.0004183404],"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":[0.0005294253,0.0002623209,0.005627833,0.001623783,0.001747891,0.001180477,0.0005276979,0.1262022,0.004866627,0.5096582,0.01323808,0.3345354],"study_design_scores_gemma":[0.00009036528,0.0001859945,0.003654164,0.0001880368,0.0004684198,0.0005023816,0.00008562829,0.4702403,0.001675225,0.5131584,0.009657556,0.00009353711],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009099144,0.002627742,0.9847068,0.001218765,0.0001546407,0.00004144708,0.0001801695,0.0001347426,0.001836487],"genre_scores_gemma":[0.3982478,0.009637154,0.573134,0.0009753935,0.003482796,0.0005216745,0.001217422,0.000350174,0.01243351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01409015,"threshold_uncertainty_score":0.07451671,"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."}}