{"id":"W2793641334","doi":"10.1177/0962280218757560","title":"Efficient robust doubly adaptive regularized regression with applications","year":2018,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Robustness (evolution); Estimator; Computer science; Oracle; Mathematical optimization; Robust regression; Linear regression; Algorithm; Regression; Monte Carlo method; Mathematics; Statistics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004226991,0.001158125,0.001679074,0.001097173,0.0003343293,0.0009286104,0.001448779,0.001388675,0.00212413],"category_scores_gemma":[0.01812339,0.0005731201,0.001081833,0.001385125,0.001036569,0.001082603,0.00199576,0.001513444,0.0005103404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006438206,"about_ca_system_score_gemma":0.0009329831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00216095,"about_ca_topic_score_gemma":0.001451023,"domain_scores_codex":[0.9974576,0.001585797,0.00009530102,0.0002595608,0.00049413,0.0001076687],"domain_scores_gemma":[0.991832,0.006122284,0.0006520451,0.0005126739,0.0007591005,0.0001220162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000114897,0.00006230273,0.0008294951,0.0002004421,0.00009192709,0.000161753,0.00005530397,0.8821544,0.001764377,0.06011944,0.001859728,0.05258597],"study_design_scores_gemma":[0.000005155589,0.00001112345,0.00005412761,0.000004989359,0.000003103044,0.00001246332,0.000002402509,0.9930977,0.0002305406,0.006221815,0.000352937,0.000003678888],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003811405,0.000361536,0.9947374,0.0001880067,0.0000233593,0.00001964149,0.00003235046,0.0001333469,0.0006929196],"genre_scores_gemma":[0.3817877,0.001365763,0.6109565,0.0002781332,0.0002328574,0.0003615048,0.0004043738,0.0002465866,0.004366623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004226991,"threshold_uncertainty_score":0.02235478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3460539560094121,"score_gpt":0.6137930848336274,"score_spread":0.2677391288242152,"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."}}