{"id":"W4285498631","doi":"10.1016/j.csda.2022.107567","title":"High-dimensional robust regression with L-loss functions","year":2022,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Outlier; Estimator; Robust regression; Least absolute deviations; Quantile regression; Robust statistics; Robustness (evolution); Regression; Penalty method; Quantile; Sample size determination; Applied mathematics; Regression analysis; Smoothness; Mathematical optimization; Statistics; Algorithm","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":[0.009499159,0.00181848,0.001871432,0.001397264,0.0005639104,0.002099472,0.003497393,0.002511042,0.003551503],"category_scores_gemma":[0.03206006,0.001017904,0.001478669,0.001804508,0.001643297,0.003541953,0.00399474,0.004010932,0.002917923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009587608,"about_ca_system_score_gemma":0.001692403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00196928,"about_ca_topic_score_gemma":0.001947967,"domain_scores_codex":[0.9949955,0.002691335,0.0003116959,0.000802501,0.0009814943,0.0002174914],"domain_scores_gemma":[0.986185,0.008553468,0.001071864,0.002479244,0.001408887,0.00030165],"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":[0.0004614437,0.0002467803,0.001489702,0.000490153,0.0003568419,0.0002104392,0.0001258426,0.5541781,0.007986879,0.1448963,0.01353933,0.2760182],"study_design_scores_gemma":[0.00001477719,0.00003757626,0.0001982429,0.00002061247,0.00001476012,0.00004169791,0.000006352936,0.963098,0.001627086,0.03363008,0.001291301,0.00001941299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001155814,0.0001529422,0.9979977,0.0001047204,0.00001914214,0.000008660055,0.00005336534,0.0003251435,0.0001823432],"genre_scores_gemma":[0.1506402,0.0008441044,0.8398116,0.0003584795,0.0003526864,0.00030689,0.001349899,0.0007937073,0.005542322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009499159,"threshold_uncertainty_score":0.050237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1353971684618698,"score_gpt":0.401045294127797,"score_spread":0.2656481256659272,"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."}}