{"id":"W2118616699","doi":"10.1016/j.jspi.2009.05.006","title":"Automatic and asymptotically optimal data sharpening for nonparametric regression","year":2009,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Control Systems and Identification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Chinese University of Hong Kong; University of Hong Kong; National Science Foundation","keywords":"Sharpening; Mathematics; Nonparametric regression; Nonparametric statistics; Smoothing; Regression; Selection (genetic algorithm); Regression analysis; Asymptotically optimal algorithm; Applied mathematics; Mathematical optimization; Algorithm; Statistics; Computer science; Artificial intelligence","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":[0.008469119,0.001101654,0.002062135,0.001846491,0.0005923756,0.001765938,0.002359976,0.001867391,0.002600364],"category_scores_gemma":[0.04264303,0.001623696,0.001555578,0.001479477,0.002925168,0.003101041,0.005118186,0.003923891,0.0007140927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012141,"about_ca_system_score_gemma":0.001840892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001377436,"about_ca_topic_score_gemma":0.001437277,"domain_scores_codex":[0.9952281,0.002347075,0.0003312425,0.0007436803,0.001068213,0.0002817515],"domain_scores_gemma":[0.9773512,0.01649262,0.001331198,0.002968613,0.001527721,0.00032868],"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":[0.0007705582,0.0001632763,0.001271062,0.0005130763,0.0002102219,0.0002238631,0.0003425531,0.3207328,0.02879714,0.2928334,0.003363916,0.3507781],"study_design_scores_gemma":[0.00003476611,0.00006310446,0.0003154814,0.00002452993,0.00002119973,0.0000781345,0.00001623683,0.8967637,0.004983282,0.09645953,0.001207283,0.00003262609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002523543,0.0001075558,0.9968742,0.00007489805,0.00001266238,0.000009647969,0.00001778279,0.000189553,0.000190034],"genre_scores_gemma":[0.2003665,0.0005065746,0.7955897,0.0002198896,0.0001684655,0.000164693,0.0002797388,0.0004036911,0.002300684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008469119,"threshold_uncertainty_score":0.04478949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035823267555427,"score_gpt":0.3221293447598987,"score_spread":0.2863060772044718,"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."}}