{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005164773,0.0003346069,0.0004658919,0.00005756699,0.0002425042,0.00004420823,0.0001692567,0.00008782522,0.00002294706],"category_scores_gemma":[0.0014868,0.0002573009,0.0000707389,0.0001322325,0.0003098286,0.0001093118,0.00003552911,0.0002190154,0.000002243371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196284,"about_ca_system_score_gemma":0.00007149706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003348179,"about_ca_topic_score_gemma":0.0000512972,"domain_scores_codex":[0.9977794,0.0001613641,0.0005865251,0.0005736967,0.0003427014,0.0005563227],"domain_scores_gemma":[0.9960025,0.002974069,0.0002466271,0.0004557601,0.0001926356,0.0001283853],"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":[0.0003135313,0.0002663137,0.0007407613,0.0009967255,0.00004722605,0.00002996505,0.000138111,0.0009307063,0.006282584,0.9746878,0.01146994,0.0040963],"study_design_scores_gemma":[0.0007444436,0.0001843915,0.0002339681,0.0001367063,0.00009793624,0.000006820067,0.000006164591,0.01573571,0.00109884,0.98088,0.0005037094,0.0003712806],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01367867,0.00001067402,0.9836003,0.0004718937,0.00009781872,0.001276729,0.0006280572,0.0001537464,0.00008210966],"genre_scores_gemma":[0.0114412,0.000001066228,0.9877387,0.0002551737,0.0001118279,0.0001985528,0.0000863251,0.0000657745,0.0001013998],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01480501,"threshold_uncertainty_score":0.9999879,"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."}}