{"id":"W2227982193","doi":"10.1007/978-3-642-15393-8_2","title":"Variable Selection by C p Statistic in Multiple Responses Regression with Fewer Sample Size Than the Dimension","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Statistic; Statistics; Sample size determination; Dimension (graph theory); Mathematics; Regression; Sample (material); Variable (mathematics); Regression analysis; Linear regression; Selection (genetic algorithm); Feature selection; Econometrics; Computer science; Artificial intelligence; Combinatorics; Chromatography; Chemistry","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":[0.04918055,0.002815041,0.004865364,0.002273019,0.001813837,0.001958145,0.00537449,0.003971255,0.008623208],"category_scores_gemma":[0.1397491,0.002067727,0.004045769,0.004982078,0.004029141,0.003782643,0.002354806,0.007620247,0.00232365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000733,"about_ca_system_score_gemma":0.00359681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002879887,"about_ca_topic_score_gemma":0.003117862,"domain_scores_codex":[0.9478524,0.04257137,0.001420317,0.004587865,0.002968647,0.0005994914],"domain_scores_gemma":[0.8017969,0.1810849,0.001801979,0.01134602,0.003365701,0.0006044859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002136256,0.0006239887,0.01234835,0.001601298,0.001641246,0.001239011,0.0004981076,0.07709429,0.007261829,0.1117544,0.03952944,0.7442718],"study_design_scores_gemma":[0.0005072827,0.0008563725,0.006382178,0.0001804384,0.0005218849,0.0005430635,0.000144078,0.8379149,0.006751718,0.1341038,0.01192829,0.0001661202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004090596,0.0004039065,0.9935771,0.000368494,0.0002508949,0.0001070617,0.000160255,0.000645495,0.0003962065],"genre_scores_gemma":[0.05597174,0.0005081151,0.9367276,0.0003780286,0.0007158645,0.001154791,0.0005198139,0.000721271,0.003302857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04918055,"threshold_uncertainty_score":0.2600947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073244923144215,"score_gpt":0.2432640323637507,"score_spread":0.2325315831323086,"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."}}