{"id":"W370182128","doi":"","title":"Robust VIF Regression","year":2011,"lang":"en","type":"article","venue":"Archive ouverte UNIGE (University of Geneva)","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Outlier; Robust regression; Robustness (evolution); Feature selection; Regression; Computer science; Estimator; Variance inflation factor; Covariate; Lasso (programming language); Regression analysis; Robust statistics; Econometrics; Artificial intelligence; Mathematics; Statistics; Machine learning; Multicollinearity","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004857108,0.001685691,0.001999,0.001723319,0.0005008319,0.001659569,0.002469236,0.001822528,0.004611662],"category_scores_gemma":[0.02156605,0.0005575189,0.001847502,0.002195466,0.0007149241,0.00165589,0.001143822,0.002448118,0.002992887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007262238,"about_ca_system_score_gemma":0.001545394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005306992,"about_ca_topic_score_gemma":0.003128747,"domain_scores_codex":[0.9972548,0.001027197,0.0001176248,0.0005874688,0.0008119631,0.000201061],"domain_scores_gemma":[0.9944596,0.003197103,0.000452893,0.0008660906,0.0009591449,0.00006514874],"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":[0.0001149746,0.00006577654,0.002415029,0.000272914,0.0003133839,0.0001510422,0.00008050722,0.530289,0.003534644,0.06789926,0.01355852,0.381305],"study_design_scores_gemma":[0.0000110792,0.00004462824,0.0008848896,0.00003359554,0.00002491998,0.00009148384,0.00001406644,0.9700949,0.001554331,0.01787392,0.009336875,0.00003541222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001836494,0.000537704,0.9956928,0.0001407256,0.00008169647,0.00002790619,0.0002110879,0.0004822481,0.0009893535],"genre_scores_gemma":[0.2078616,0.002563849,0.7712626,0.0003061915,0.0004903576,0.0003610264,0.003093942,0.001175855,0.01288458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005306992,"threshold_uncertainty_score":0.02568716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087734962141823,"score_gpt":0.1584069358996747,"score_spread":0.1375295862782564,"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."}}