{"id":"W3125019936","doi":"","title":"Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Nonparametric regression; Mathematics; Nonparametric statistics; Kernel smoother; Estimator; Kernel (algebra); Consistency (knowledge bases); Univariate; Kernel regression; Kernel method; Semiparametric regression; Constraint (computer-aided design); Applied mathematics; Multivariate statistics; Econometrics; Mathematical optimization; Statistics; Computer science; Artificial intelligence; Support vector machine; Radial basis function kernel; Discrete mathematics","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":[0.009003156,0.001139006,0.002007275,0.001045318,0.0007476228,0.002112711,0.002899211,0.002200035,0.002776752],"category_scores_gemma":[0.05266935,0.0007723012,0.001284509,0.002993596,0.002391655,0.003918872,0.003108464,0.003413008,0.0007916515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008686694,"about_ca_system_score_gemma":0.001883868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002757507,"about_ca_topic_score_gemma":0.002130226,"domain_scores_codex":[0.9934303,0.003983641,0.0002322236,0.0008050404,0.001213453,0.0003353327],"domain_scores_gemma":[0.9718195,0.01959693,0.002973803,0.003677024,0.001566701,0.0003660176],"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":[0.0002036648,0.0001332174,0.007411755,0.0003361713,0.0002166694,0.0006531927,0.0002018938,0.3497864,0.003137245,0.5206866,0.002100528,0.1151327],"study_design_scores_gemma":[0.00002509932,0.00004733079,0.001540523,0.00002753988,0.00002850393,0.0001274172,0.00003070052,0.8262041,0.0009446116,0.1693667,0.001624671,0.00003273106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01613468,0.0003394731,0.9819452,0.0002980731,0.00002950606,0.00002182157,0.00007763557,0.0001073235,0.001046352],"genre_scores_gemma":[0.666375,0.001659659,0.3242588,0.0002888878,0.0003614185,0.0002749047,0.000617362,0.0001812521,0.005982647],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009003156,"threshold_uncertainty_score":0.04761386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1073426015299481,"score_gpt":0.3455397766133362,"score_spread":0.2381971750833881,"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."}}