{"id":"W2010622967","doi":"10.1016/j.jmva.2004.09.004","title":"Estimation of the mean vector of a multivariate normal distribution: subspace hypothesis","year":2004,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Multivariate normal distribution; Statistics; Mean squared error; Efficient estimator; James–Stein estimator; Consistent estimator; Bayes estimator; Covariance matrix; Bias of an estimator; Invariant estimator; Minimum-variance unbiased estimator; Trimmed estimator; Estimation of covariance matrices; Wishart distribution; Normal distribution; Multivariate statistics","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":[0.0006603644,0.0001606342,0.0005914737,0.000164628,0.0001242054,0.00002079885,0.0003256621,0.00009008562,0.0001368815],"category_scores_gemma":[0.002970237,0.000110686,0.0006124237,0.001515935,0.0001411853,0.0001569826,0.00004648871,0.0001846498,0.00000435428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001521543,"about_ca_system_score_gemma":0.0001380734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00015016,"about_ca_topic_score_gemma":0.0000328037,"domain_scores_codex":[0.9977392,0.0001634434,0.001202299,0.000136596,0.0005942449,0.0001642271],"domain_scores_gemma":[0.9961085,0.0008272371,0.001863736,0.0003661128,0.0007300949,0.000104361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001994224,0.001792381,0.001032813,0.0001687264,0.003480963,0.000004929422,0.001716879,0.1318555,0.0125796,0.8431828,0.0002207073,0.00376525],"study_design_scores_gemma":[0.005519283,0.0002096681,0.5365712,0.0003885435,0.01240036,0.00004068379,0.0006472106,0.2231321,0.08255847,0.1378069,0.0001711015,0.0005543978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1785149,0.00001409158,0.820017,0.0008664418,0.0000481047,0.0001316553,0.0003206498,0.00001030854,0.00007687776],"genre_scores_gemma":[0.945585,0.000005594356,0.0542854,0.00001454783,0.00002957767,0.000005364187,0.00002495498,0.000009878008,0.00003966005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7670702,"threshold_uncertainty_score":0.4513642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04236590469899074,"score_gpt":0.332789307935335,"score_spread":0.2904234032363442,"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."}}