{"id":"W953300354","doi":"10.1007/978-3-7908-2604-3_6","title":"Robust Model Selection with LARS Based on S-estimators","year":2010,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Covariate; Estimator; Selection (genetic algorithm); Regression; Computer science; Model selection; Regression analysis; Lasso (programming language); Greedy algorithm; Mathematics; Statistics; Econometrics; Data mining; Algorithm; 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":[0.0001795642,0.0003943847,0.0004515084,0.000112227,0.00009983793,0.0000231872,0.0001063095,0.0004261314,0.0005814974],"category_scores_gemma":[0.000159753,0.0002828729,0.00008293339,0.00002034646,0.00008457207,0.00004504603,0.0000183125,0.0007936514,0.00002702771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005898606,"about_ca_system_score_gemma":0.0001113747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000229946,"about_ca_topic_score_gemma":0.00005045283,"domain_scores_codex":[0.9986831,0.00001340152,0.0002673884,0.0004452165,0.0003597966,0.0002311116],"domain_scores_gemma":[0.9986,0.0005870262,0.0001671406,0.0003642844,0.000141937,0.0001396264],"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":[0.00006025194,0.00003299773,2.502627e-7,0.00007043409,0.00001979536,0.000006799541,0.000006418633,0.03309998,0.00002285359,0.963719,0.0004980331,0.002463226],"study_design_scores_gemma":[0.0001744827,0.0001156591,8.273479e-8,0.00009948467,0.00006837813,0.000003353544,5.162252e-7,0.4326781,0.00008838317,0.565172,0.001351366,0.0002481634],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001802676,0.000001283335,0.616262,0.00003724958,0.00003688237,0.00021307,0.00003504315,0.0001229278,0.3832898],"genre_scores_gemma":[0.0001049049,0.000002022134,0.7275147,0.0001913606,0.00004618571,0.00001593112,0.00001108405,0.000111932,0.2720019],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3995782,"threshold_uncertainty_score":0.9999623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1515878373107531,"score_gpt":0.3662446472880066,"score_spread":0.2146568099772534,"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."}}