{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005610098,0.001385064,0.002394619,0.001568311,0.0004597641,0.001527414,0.00204163,0.001486929,0.002573434],"category_scores_gemma":[0.01591723,0.0008576988,0.001854347,0.001812431,0.001029837,0.001467303,0.002025204,0.002041313,0.001439885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006281931,"about_ca_system_score_gemma":0.001570432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00229959,"about_ca_topic_score_gemma":0.002048201,"domain_scores_codex":[0.997147,0.001960443,0.0001072876,0.0002817061,0.0003903872,0.0001130835],"domain_scores_gemma":[0.9922266,0.006207599,0.0004653347,0.0004870672,0.0004978033,0.0001156014],"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":[0.0002057988,0.00009620843,0.001353946,0.0003129258,0.0004063585,0.0002536812,0.0001014877,0.7319419,0.001853806,0.07927258,0.004052227,0.1801491],"study_design_scores_gemma":[0.00002115544,0.00005274446,0.0001192516,0.00001632816,0.00001864569,0.00003273123,0.000008507825,0.9673016,0.0004354061,0.03090397,0.001070365,0.00001928476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002072876,0.0002603367,0.9968994,0.0001041576,0.00002341181,0.00001983118,0.00003666971,0.0002201914,0.0003630402],"genre_scores_gemma":[0.1450095,0.001261057,0.8468742,0.0003854135,0.0003452834,0.000436526,0.0007973415,0.0003029327,0.004587725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005610098,"threshold_uncertainty_score":0.02966934,"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."}}