{"id":"W1994727681","doi":"10.1006/jmva.2000.1915","title":"Robust Improvement in Estimation of a Mean Matrix in an Elliptically Contoured Distribution","year":2001,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Robustness (evolution); Elliptical distribution; Minimax; Statistics; Multivariate statistics; Applied mathematics; Scatter matrix; Linear regression; Distribution (mathematics); Multivariate normal distribution; Mathematical analysis; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001654597,0.0001207957,0.0006639239,0.0003540562,0.00001922801,0.00001684373,0.0001263763,0.00007811945,0.00003848718],"category_scores_gemma":[0.001262065,0.00009722901,0.0001765691,0.0007256121,0.00002673313,0.0002440622,0.00001958644,0.0001991374,3.220933e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001648635,"about_ca_system_score_gemma":0.00003668091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001483002,"about_ca_topic_score_gemma":0.000486722,"domain_scores_codex":[0.9978899,0.0002362079,0.001222888,0.000143082,0.0003226592,0.0001852832],"domain_scores_gemma":[0.9981985,0.0005297807,0.0007235304,0.0001702973,0.0002805937,0.00009730355],"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":[0.0009466924,0.002152946,0.004794916,0.00009423713,0.0007651711,0.0001542567,0.001546031,0.8645865,0.01703986,0.05226765,0.000008021044,0.05564378],"study_design_scores_gemma":[0.001517926,0.0002726795,0.009357846,0.00006938064,0.0007018208,0.000003994179,0.0002266337,0.8808096,0.0004459113,0.106476,0.00000468993,0.0001134831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3533522,0.00001357305,0.6464558,0.00005563973,0.00001606947,0.00007693154,0.00001355915,0.000002609071,0.00001373026],"genre_scores_gemma":[0.6309826,0.00001951178,0.3689486,0.000004737432,0.00001571717,0.000001875314,0.000008453587,0.000005600784,0.00001296151],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2776304,"threshold_uncertainty_score":0.3964884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08263484248561455,"score_gpt":0.4211029445160301,"score_spread":0.3384681020304156,"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."}}