{"id":"W2798517237","doi":"10.1002/sta4.316","title":"Nonasymptotic support recovery for high‐dimensional sparse covariance matrices","year":2020,"lang":"en","type":"article","venue":"Stat","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Covariance; Covariance matrix; Estimation of covariance matrices; Matrix norm; Computation; Regularization (linguistics); Thresholding","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009596134,0.0008839525,0.001138293,0.0009985688,0.0006465138,0.001115518,0.001652127,0.001567126,0.001771192],"category_scores_gemma":[0.06469436,0.00062095,0.0008740941,0.001096891,0.003279776,0.002359271,0.002777356,0.002889053,0.0005247501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005697965,"about_ca_system_score_gemma":0.001251485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008506865,"about_ca_topic_score_gemma":0.0010207,"domain_scores_codex":[0.9967738,0.001834765,0.0001575305,0.0004149777,0.0006756736,0.0001432204],"domain_scores_gemma":[0.9622176,0.03101679,0.002371599,0.002859096,0.001137678,0.0003972525],"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":[0.0004091329,0.0001662483,0.005912515,0.00059145,0.0002428194,0.0005837774,0.0004714839,0.4722087,0.01495016,0.3144336,0.004382795,0.1856474],"study_design_scores_gemma":[0.00002540904,0.00004607859,0.0006592674,0.00003403579,0.000008255016,0.0001282463,0.00004563048,0.8794216,0.002659219,0.1162525,0.000701383,0.00001833283],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008257823,0.0001084288,0.9910401,0.0001810197,0.0000105746,0.0000164648,0.00003695061,0.00008800648,0.0002607049],"genre_scores_gemma":[0.3981911,0.0006223092,0.5977423,0.0003360692,0.0001296687,0.0003149305,0.0006173032,0.0001965791,0.001849809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009596134,"threshold_uncertainty_score":0.05074984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1169873063794826,"score_gpt":0.3572124556471022,"score_spread":0.2402251492676196,"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."}}