{"id":"W2054045791","doi":"10.1109/tsp.2012.2210546","title":"Shrinkage-to-Tapering Estimation of Large Covariance Matrices","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Estimator; Mathematics; Covariance; Covariance matrix; Estimation of covariance matrices; Shrinkage estimator; Applied mathematics; Tapering; Statistics; Algorithm; Combinatorics; Bias of an estimator; Computer science; Minimum-variance unbiased estimator","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":[0.0003693843,0.0001466661,0.0002181475,0.0001649132,0.0002842126,0.00004488069,0.0001404439,0.00006688888,0.0001387323],"category_scores_gemma":[0.00000925701,0.000137417,0.00008059252,0.0005610993,0.0000219483,0.0004161671,0.000001565759,0.0001508824,0.00005315135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003782464,"about_ca_system_score_gemma":0.00003264357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005675199,"about_ca_topic_score_gemma":0.000002026991,"domain_scores_codex":[0.9988806,0.00002820701,0.0003657097,0.0001685981,0.0002617863,0.0002951169],"domain_scores_gemma":[0.9992495,0.0002187176,0.0001729126,0.0001708025,0.00007635196,0.0001117607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000531252,0.004556888,0.0001837631,0.003784752,0.0002246962,0.000003328081,0.01391238,0.2213761,0.05246138,0.01674991,0.0006015396,0.685614],"study_design_scores_gemma":[0.005701904,0.0004006604,0.0007497047,0.001991194,0.001008022,0.00008622776,0.002478094,0.419345,0.5418586,0.01947972,0.004873371,0.002027584],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07916973,0.0001290771,0.9194447,0.00008534612,0.00006721915,0.0002782849,0.00001852888,0.0001089295,0.0006981717],"genre_scores_gemma":[0.9342589,0.000008904713,0.06535346,0.00005457158,0.00006532432,0.00008675566,9.517047e-7,0.00002760926,0.0001435151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8550892,"threshold_uncertainty_score":0.5603701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03358121091171269,"score_gpt":0.3291282374208948,"score_spread":0.2955470265091821,"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."}}