{"id":"W2950647778","doi":"10.48550/arxiv.1406.6031","title":"Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Multivariate statistics; Estimator; Weighting; Scatter matrix; Robust statistics; Computer science; Statistics; Estimation; Multivariate analysis; Data mining; Multivariate normal distribution; Econometrics; Mathematics; Artificial intelligence; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.000532616,0.0001131243,0.0002366982,0.00008970159,0.00002727351,0.000007993029,0.000116291,0.0001070932,0.000002304828],"category_scores_gemma":[0.0006030951,0.00009890344,0.00002121066,0.0001027046,0.0001547441,0.00008763905,0.000134083,0.0001564753,1.801285e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002321275,"about_ca_system_score_gemma":0.00002031442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001279689,"about_ca_topic_score_gemma":0.00004194003,"domain_scores_codex":[0.9991239,0.0002644727,0.0002064707,0.0002682633,0.00005505059,0.00008186463],"domain_scores_gemma":[0.9979868,0.001296342,0.0002997736,0.0002621358,0.000127766,0.00002721314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007075567,0.0001261517,0.00059147,0.001522026,0.0000206047,0.00001100549,0.001723739,0.3773698,0.0001578643,0.6137959,0.00000986785,0.004600761],"study_design_scores_gemma":[0.0002959891,0.00002485995,0.002559872,0.0001885066,0.00006483029,0.000001043772,0.0001261855,0.6638187,0.0001438856,0.3327038,0.000001004244,0.00007134322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3454196,0.00001304768,0.6541942,0.00001866012,0.00001760543,0.0002459166,0.00001054291,0.000004256674,0.00007620501],"genre_scores_gemma":[0.939589,0.00004188202,0.06029013,0.000008458638,0.00000556539,0.000001494816,0.000006658531,0.0000067366,0.00005002883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5941694,"threshold_uncertainty_score":0.4033165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.178487240646816,"score_gpt":0.292794812477478,"score_spread":0.114307571830662,"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."}}