{"id":"W2751380915","doi":"10.1080/00949655.2017.1371174","title":"Improved robust ridge M-estimation","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Multicollinearity; Estimator; Mathematics; Robust regression; Outlier; Ordinary least squares; Robust statistics; Statistics; Ridge; Regression; Monte Carlo method; Shrinkage estimator; Elastic net regularization; Regression analysis; Minimum-variance unbiased estimator; Efficient estimator; Geography","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":[0.0005827888,0.000102849,0.0002621092,0.00006430177,0.0002973977,0.000150168,0.00007753971,0.00005525631,0.00002056728],"category_scores_gemma":[0.005125957,0.00008508567,0.00003499441,0.00002346794,0.00009782808,0.000395866,0.00002816932,0.0001546917,0.000001082046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002962723,"about_ca_system_score_gemma":0.00002757746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002870217,"about_ca_topic_score_gemma":0.000001323587,"domain_scores_codex":[0.9989174,0.00009175521,0.0005351222,0.0001154878,0.0002255948,0.0001146193],"domain_scores_gemma":[0.9966042,0.002112353,0.0006999747,0.0001048494,0.0003531752,0.0001254531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001965959,0.00009948375,0.00011087,0.0001049699,0.00003453576,0.00001903123,0.000220589,0.3265602,0.0001717315,0.2919631,0.0001636466,0.3803553],"study_design_scores_gemma":[0.0004932627,0.0001093084,0.00293226,0.00002299296,0.00003176987,0.000008412874,0.00001352581,0.5842221,0.00001118519,0.4120758,0.00002299474,0.00005638518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01482762,0.00001289921,0.9844822,0.000172624,0.0001662151,0.0001119332,0.00002776933,0.00001172264,0.0001870476],"genre_scores_gemma":[0.5052815,0.000003346702,0.4946271,0.00001845278,0.00004658534,4.869187e-7,0.000003292916,0.000006481643,0.00001277735],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4904539,"threshold_uncertainty_score":0.6136619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1605318197642338,"score_gpt":0.4726641449471108,"score_spread":0.3121323251828769,"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."}}