{"id":"W1976889927","doi":"10.1016/j.jmva.2008.06.006","title":"A test for the mean vector with fewer observations than the dimension under non-normality","year":2008,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":128,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Combinatorics; Test statistic; Dimension (graph theory); Covariance matrix; Null (SQL); Asymptotic distribution; Null distribution; Multivariate random variable; Statistics; Sample mean and sample covariance; Statistic; Multivariate normal distribution; Independent and identically distributed random variables; Normality; Matrix (chemical analysis); Random variable; Statistical hypothesis testing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03458287,0.001304352,0.003852315,0.003485654,0.001710473,0.00348043,0.003845469,0.004386895,0.008608835],"category_scores_gemma":[0.2178634,0.0007360363,0.002240747,0.003178885,0.005902112,0.008057981,0.003672389,0.003958794,0.001091418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007949352,"about_ca_system_score_gemma":0.002822194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005895928,"about_ca_topic_score_gemma":0.0004669551,"domain_scores_codex":[0.9523392,0.02631455,0.003300965,0.009213637,0.007463679,0.001368082],"domain_scores_gemma":[0.5412366,0.4168724,0.01129489,0.01877978,0.008694785,0.003121481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01547181,0.001718768,0.2597195,0.001655694,0.005842292,0.001961361,0.001618049,0.03199071,0.0414847,0.1403181,0.008536724,0.4896823],"study_design_scores_gemma":[0.003277314,0.01419988,0.1580885,0.0003637822,0.001774062,0.00487738,0.002667572,0.5147405,0.0259137,0.2621092,0.01128789,0.0007001728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3541217,0.000531058,0.6367294,0.002035502,0.0005117047,0.0002736688,0.001354829,0.0009209085,0.003521269],"genre_scores_gemma":[0.8718129,0.0001648617,0.1220557,0.0007489327,0.0007197732,0.0005534451,0.0022122,0.0001821771,0.001549962],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03458287,"threshold_uncertainty_score":0.1828939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07456729812169502,"score_gpt":0.3212771450148324,"score_spread":0.2467098468931374,"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."}}