{"id":"W1574204554","doi":"10.1002/cjs.11181","title":"On central matrix based methods in dimension reduction","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sliced inverse regression; Dimension (graph theory); Inverse; Variance (accounting); Sufficient dimension reduction; Matrix (chemical analysis); Computer science; Dimensionality reduction; Variance reduction; Mathematics; Regression; Reduction (mathematics); Statistics; Constant (computer programming); Econometrics; Mathematical optimization; Algorithm; Artificial intelligence; Monte Carlo method","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007771107,0.001446709,0.001508928,0.002551816,0.001039741,0.001798277,0.002606293,0.001525163,0.004482933],"category_scores_gemma":[0.03443888,0.000931442,0.001482347,0.00216203,0.003150952,0.00303319,0.004846829,0.00349512,0.002284577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009778398,"about_ca_system_score_gemma":0.001948553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002064788,"about_ca_topic_score_gemma":0.00264326,"domain_scores_codex":[0.9943262,0.003469119,0.000197968,0.0006456398,0.001155391,0.0002058145],"domain_scores_gemma":[0.9805225,0.01288375,0.0008872497,0.002478066,0.002765197,0.0004631677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003380495,0.0002011465,0.001605849,0.000489797,0.0002349482,0.0001455388,0.0004253266,0.2259651,0.006743016,0.5010027,0.01028399,0.2525646],"study_design_scores_gemma":[0.00004172046,0.00008808551,0.000217433,0.00005599771,0.00002627244,0.00008675659,0.00003909084,0.7823275,0.002433421,0.2100621,0.004584686,0.00003694924],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001445557,0.0002363119,0.9975483,0.00008620061,0.00004829103,0.00002181221,0.00001965289,0.0001643865,0.0004294671],"genre_scores_gemma":[0.05234021,0.0004033641,0.9437332,0.0002382441,0.0002181012,0.0003046073,0.0001727873,0.0003193819,0.002270142],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007771107,"threshold_uncertainty_score":0.041098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07847587495821476,"score_gpt":0.3838524638124837,"score_spread":0.305376588854269,"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."}}