{"id":"W2114787170","doi":"10.1080/10629360600687840","title":"Some extensions of multivariate sliced inverse regression","year":2006,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Windsor","keywords":"Sliced inverse regression; Mathematics; Multivariate statistics; Sufficient dimension reduction; Statistics; Regression; Inverse; Dimension (graph theory); Regression analysis; Parametric statistics; Econometrics; Combinatorics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.005888955,0.000945478,0.0008633088,0.001095933,0.0003729593,0.0009099321,0.001528602,0.000663959,0.00473254],"category_scores_gemma":[0.01665363,0.0005951631,0.001830885,0.001311468,0.001602287,0.001808415,0.002722983,0.0020778,0.0004318708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007987706,"about_ca_system_score_gemma":0.001386304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003906931,"about_ca_topic_score_gemma":0.003128557,"domain_scores_codex":[0.9973322,0.001459587,0.0001432282,0.0003810986,0.0005210686,0.0001627293],"domain_scores_gemma":[0.991967,0.00383582,0.0007676163,0.001805931,0.001328768,0.000294882],"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":[0.0002082156,0.00007565172,0.004836509,0.0002826932,0.0002559326,0.0004564091,0.00051884,0.2817399,0.005429138,0.5225222,0.00439865,0.1792758],"study_design_scores_gemma":[0.00002666178,0.0001184346,0.001565567,0.00006683074,0.00005507364,0.0002201379,0.00005592946,0.7837278,0.002034184,0.2020122,0.01007572,0.00004141503],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00770575,0.0002971683,0.9898751,0.0001542164,0.00003788025,0.00001971922,0.00007019505,0.0001868743,0.001652966],"genre_scores_gemma":[0.3347977,0.001019518,0.6583365,0.0003056045,0.0002669603,0.0001847918,0.0003898487,0.0002440456,0.004455111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005888955,"threshold_uncertainty_score":0.03114414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08947264411057487,"score_gpt":0.4176726764295682,"score_spread":0.3282000323189934,"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."}}