{"id":"W2607428535","doi":"10.5539/ijsp.v6n3p132","title":"Self-Selecting Robust Logistic Regression Model","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"African Union Commission; African Union","keywords":"Trimming; Outlier; Leverage (statistics); Logistic regression; Computer science; Robust regression; Bayesian probability; Statistics; Statistical model; Data mining; Binary data; Mathematics; Binary number","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":[0.007173617,0.001311281,0.002494212,0.002339955,0.0006117553,0.002219285,0.004957049,0.00220366,0.009256234],"category_scores_gemma":[0.02344059,0.0008356773,0.002741375,0.002306846,0.0008257456,0.002323156,0.001919206,0.002043902,0.003903399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009865382,"about_ca_system_score_gemma":0.001273939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005138032,"about_ca_topic_score_gemma":0.002451304,"domain_scores_codex":[0.9931648,0.00349635,0.0003025279,0.001618315,0.0009866694,0.0004313],"domain_scores_gemma":[0.9901007,0.006032065,0.001171354,0.001178485,0.001329027,0.000188522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006831732,0.0002681285,0.01450216,0.0005260287,0.0007764146,0.001974141,0.0005071495,0.577806,0.003262868,0.1937189,0.01573598,0.190239],"study_design_scores_gemma":[0.00004372731,0.00008138714,0.001340713,0.00004219962,0.0001042907,0.0003749531,0.00004097268,0.9517882,0.0006312116,0.0409651,0.004533734,0.00005354977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01996892,0.0005186006,0.9736555,0.0004046054,0.00007166424,0.0001438788,0.0007935879,0.001228458,0.003214865],"genre_scores_gemma":[0.6313099,0.001775133,0.3376307,0.0004836954,0.0004263837,0.001184625,0.004291737,0.0008162407,0.02208163],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009256234,"threshold_uncertainty_score":0.03793812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2164328713275842,"score_gpt":0.4602158475967824,"score_spread":0.2437829762691982,"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."}}