{"id":"W2161686623","doi":"10.2307/3316042","title":"Robust linear discriminant analysis using S‐estimators","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Outlier; M-estimator; Mathematics; Discriminant function analysis; Invariant estimator; Statistics; Linear discriminant analysis; Trimmed estimator; Robust statistics; Minimax estimator; Efficient estimator; Covariance matrix; Minimum-variance unbiased estimator","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":[0.007832706,0.001152951,0.001475237,0.002596459,0.0004757908,0.001900692,0.001200157,0.001220135,0.002276857],"category_scores_gemma":[0.0246019,0.0004448513,0.001387595,0.001817784,0.001477033,0.001588186,0.002026075,0.001453059,0.001300876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008988698,"about_ca_system_score_gemma":0.0008378222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001532648,"about_ca_topic_score_gemma":0.0007105779,"domain_scores_codex":[0.9950546,0.002857106,0.000215415,0.0006707829,0.001037667,0.0001643439],"domain_scores_gemma":[0.9882664,0.007313233,0.001054421,0.001081757,0.002092933,0.0001912801],"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":[0.0003802562,0.00007925313,0.00226209,0.0003026637,0.0004378128,0.0001431303,0.0001199494,0.4592764,0.008073549,0.2114982,0.005568508,0.3118581],"study_design_scores_gemma":[0.00002487899,0.00005791561,0.000509225,0.00002801631,0.00003444462,0.000040828,0.00001426792,0.9380358,0.002448125,0.05623534,0.002542682,0.00002845498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003474379,0.0003382763,0.9951882,0.0001051453,0.00003647723,0.00001406768,0.00002741161,0.0001132683,0.0007027498],"genre_scores_gemma":[0.3558742,0.001343829,0.6355793,0.0002926165,0.0006254115,0.000200663,0.000421503,0.0002679424,0.005394631],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007832706,"threshold_uncertainty_score":0.0414238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2383924046562461,"score_gpt":0.4046503756290366,"score_spread":0.1662579709727905,"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."}}