{"id":"W2107972492","doi":"10.1002/cjs.5550360114","title":"Logistic discrimination using robust estimators: An influence function approach","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vlaamse regering; KU Leuven","keywords":"Logistic regression; Estimator; Statistics; Mathematics; Logistic function; Function (biology); Logistic distribution; Set (abstract data type); Econometrics; Pattern recognition (psychology); Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.02293651,0.001419707,0.001912934,0.005321339,0.0006937727,0.002237405,0.002552267,0.002224625,0.002656725],"category_scores_gemma":[0.1252478,0.0007050142,0.002119283,0.002236033,0.002527857,0.002990046,0.003120413,0.002208832,0.0005392802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001458687,"about_ca_system_score_gemma":0.0007490715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001215294,"about_ca_topic_score_gemma":0.0005993165,"domain_scores_codex":[0.9881217,0.007773038,0.0003707538,0.0009989844,0.002398634,0.0003369355],"domain_scores_gemma":[0.8710608,0.1140511,0.004369394,0.004954788,0.004987196,0.000576699],"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":[0.0002970927,0.0001722136,0.00983787,0.0003973812,0.0004704908,0.0006517806,0.0004480586,0.4632463,0.0051803,0.3540635,0.001851073,0.163384],"study_design_scores_gemma":[0.00001492412,0.00007722652,0.00152921,0.00005975672,0.00006237448,0.0001643319,0.00002814748,0.9325227,0.002123883,0.06238607,0.0009948519,0.00003652451],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01294975,0.0003760705,0.9847206,0.0001428328,0.00002684612,0.00004841048,0.00002172718,0.000112868,0.001600936],"genre_scores_gemma":[0.6644653,0.0009648674,0.3312234,0.0002132641,0.0003307338,0.0003043769,0.0001810603,0.0002467945,0.002070263],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02293651,"threshold_uncertainty_score":0.1213013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.32249829488737,"score_gpt":0.3911847139157397,"score_spread":0.06868641902836975,"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."}}