{"id":"W1991446376","doi":"10.1016/j.spl.2006.01.008","title":"Bootstrapping MM-estimators for linear regression with fixed designs","year":2006,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bootstrapping (finance); Mathematics; Outlier; Estimator; Robust regression; Statistics; Robust statistics; Consistency (knowledge bases); Confidence interval; Linear regression; Strong consistency; Regression analysis; Inference; Econometrics; Computer science; 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":[0.04187359,0.001592642,0.00279989,0.00237322,0.000872762,0.00157439,0.006007432,0.003454284,0.005693984],"category_scores_gemma":[0.2134158,0.001646678,0.002813247,0.002850354,0.001712847,0.002808013,0.002897656,0.003389353,0.002271463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009288192,"about_ca_system_score_gemma":0.001572686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008733223,"about_ca_topic_score_gemma":0.001173934,"domain_scores_codex":[0.9626551,0.03140724,0.001148956,0.002477676,0.001883506,0.0004275794],"domain_scores_gemma":[0.8527988,0.1197848,0.004551195,0.01801291,0.004166098,0.0006862595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001235269,0.0003438579,0.005622059,0.001243573,0.001404887,0.0003234617,0.0004205997,0.1304153,0.005368513,0.305997,0.007863531,0.539762],"study_design_scores_gemma":[0.0003249976,0.0004227376,0.002209072,0.0003334229,0.0003052286,0.0003172642,0.00008346176,0.6106963,0.004549569,0.3712059,0.009468246,0.00008374992],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001891208,0.000167474,0.9973967,0.0000478572,0.00003370455,0.00005276753,0.00004546152,0.000194121,0.0001707361],"genre_scores_gemma":[0.06546029,0.0003773988,0.9307877,0.0001942138,0.0001496606,0.001168155,0.0005428369,0.0002222584,0.001097417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04187359,"threshold_uncertainty_score":0.2214513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1325593805198238,"score_gpt":0.3973015978866078,"score_spread":0.264742217366784,"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."}}