{"id":"W1973650202","doi":"10.1002/cjs.10105","title":"Robust penalized logistic regression with truncated loss functions","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Outlier; Logistic regression; Computer science; Robust regression; Regression; Statistics; Selection (genetic algorithm); Logistic model tree; Mathematics; Artificial intelligence; Function (biology); Robustness (evolution); Machine learning; Pattern recognition (psychology); Econometrics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01525869,0.001993518,0.002364811,0.001539124,0.0005300774,0.002219399,0.003845397,0.002648094,0.002781025],"category_scores_gemma":[0.04248699,0.0009518094,0.002277094,0.001606958,0.00147031,0.003091658,0.002768157,0.004395561,0.002055551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277352,"about_ca_system_score_gemma":0.001595558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00330479,"about_ca_topic_score_gemma":0.002091069,"domain_scores_codex":[0.9920579,0.004561648,0.0004139681,0.00122338,0.001303859,0.0004394224],"domain_scores_gemma":[0.9793102,0.01355285,0.002171961,0.002533081,0.0021239,0.0003078952],"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":[0.0005992413,0.0001158707,0.004388598,0.0002724568,0.0002942703,0.0003217983,0.0001144742,0.7971322,0.003924274,0.02361823,0.007990936,0.1612277],"study_design_scores_gemma":[0.00002197815,0.00005450899,0.0005297435,0.00002368251,0.00001876867,0.00007205721,0.000009540863,0.9896102,0.0008609084,0.007770169,0.001004375,0.00002413874],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01117584,0.0006473023,0.9860902,0.0004619717,0.00005374055,0.00005825165,0.0001847792,0.0008321111,0.0004957996],"genre_scores_gemma":[0.4561976,0.001256094,0.5292382,0.0008379619,0.0004378634,0.0006624873,0.002112411,0.0007279123,0.008529606],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01525869,"threshold_uncertainty_score":0.08069658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2950055598822325,"score_gpt":0.3629987338165997,"score_spread":0.06799317393436721,"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."}}