{"id":"W4321375019","doi":"10.3390/math11041022","title":"High-Dimensional Covariance Estimation via Constrained Lq-Type Regularization","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Estimation of covariance matrices; Mathematics; Covariance matrix; Smoothing; Covariance; Mathematical optimization; Estimator; Covariance function; Regularization (linguistics); Rational quadratic covariance function; Rank (graph theory); Matrix (chemical analysis); Applied mathematics; Matérn covariance function; Covariance intersection; Algorithm; Computer science; Statistics; Artificial intelligence; Combinatorics","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":[0.00009435567,0.00009893699,0.0001259027,0.0000754041,0.00004673028,0.00002486558,0.00006613317,0.00006601898,0.00003468986],"category_scores_gemma":[0.00005267984,0.0001004025,0.00002078117,0.0003027238,0.00002583996,0.0000608059,0.00002075067,0.00006290908,0.0001961047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002143596,"about_ca_system_score_gemma":0.000009192633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001832863,"about_ca_topic_score_gemma":4.853763e-7,"domain_scores_codex":[0.9994624,0.000008387822,0.0001744181,0.00008568222,0.0001419556,0.0001271475],"domain_scores_gemma":[0.9996281,0.00005560138,0.0000350309,0.0001955474,0.000059454,0.00002625994],"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":[0.000005596315,0.00004635465,0.000008246104,0.000174666,0.0000752256,0.00003307244,0.0004061103,0.7882186,0.1111863,0.06597888,0.02289291,0.01097402],"study_design_scores_gemma":[0.00008238753,0.00001138294,0.00004825689,0.00007385769,0.00001183359,0.00001277723,0.000005656841,0.8913718,0.02976394,0.07840858,0.0001057561,0.0001037851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07253513,0.00002279602,0.9232962,0.00009374152,0.000378802,0.000174905,0.000004644931,0.002600865,0.0008929528],"genre_scores_gemma":[0.7715053,0.000008843635,0.2280717,0.00003088449,0.00004336558,0.00000642846,0.00007460383,0.00003267446,0.0002261802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6989702,"threshold_uncertainty_score":0.4094297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654719794691968,"score_gpt":0.2304819329825737,"score_spread":0.213934735035654,"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."}}