{"id":"W1999985320","doi":"10.1198/jasa.2011.tm09650","title":"Fast Robust Model Selection in Large Datasets","year":2011,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Estimator; Outlier; Covariate; Robust regression; Robustness (evolution); Selection (genetic algorithm); Computer science; Model selection; Robust statistics; Context (archaeology); Linear regression; Ordinary least squares; Feature selection; Regression; Statistic; Mathematics; Statistics; 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":[],"consensus_categories":[],"category_scores_codex":[0.001287078,0.0001091524,0.0003759997,0.00006855569,0.00006917283,0.00001446347,0.0001852877,0.00003955077,0.00003952716],"category_scores_gemma":[0.005487601,0.00007546337,0.00006853222,0.0002726502,0.00005097628,0.0001529761,0.00005059926,0.0003978996,0.000002871148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003989439,"about_ca_system_score_gemma":0.00007085111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003060412,"about_ca_topic_score_gemma":0.0000788464,"domain_scores_codex":[0.9982176,0.0003723415,0.0005803186,0.0001188058,0.0004352674,0.0002755981],"domain_scores_gemma":[0.9973028,0.001153843,0.001196082,0.00009989982,0.0001633523,0.00008404673],"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":[0.0008460861,0.002173155,0.04780191,0.00008197164,0.0003026749,0.00004920191,0.002376446,0.006450147,0.001238483,0.8616538,0.02831274,0.04871339],"study_design_scores_gemma":[0.0006084786,0.0002025535,0.05012351,0.00003846604,0.0001179863,0.00001478779,0.0001831975,0.1181066,0.0001147844,0.8302403,0.00009593707,0.0001534614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02226488,0.000003697775,0.976631,0.0001360377,0.00008020182,0.00008873836,0.0004403824,0.000008028127,0.0003470368],"genre_scores_gemma":[0.3181109,0.000009493231,0.6815645,0.0001566119,0.00004628065,0.000002624089,0.000003089521,0.00001381801,0.00009271374],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.295846,"threshold_uncertainty_score":0.6569567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09881432076865368,"score_gpt":0.3959868610863974,"score_spread":0.2971725403177436,"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."}}