{"id":"W2267097627","doi":"10.58079/ourl","title":"Some heuristics about local regression and kernel smoothing","year":2013,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; AXA Research Fund","keywords":"Polynomial regression; Mathematics; Polynomial; Principal component regression; Linear regression; Proper linear model; Nonparametric regression; Extension (predicate logic); Kernel regression; Linear model; Applied mathematics; Linear predictor function; Statistics; Regression analysis; Regression; Computer science","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":[0.007369895,0.001485287,0.001565766,0.002322177,0.001499665,0.003103294,0.003724273,0.002616268,0.01403856],"category_scores_gemma":[0.03128029,0.0009942629,0.002118406,0.003931781,0.002935499,0.006067139,0.003222164,0.003972536,0.005341465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835088,"about_ca_system_score_gemma":0.001412806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005373945,"about_ca_topic_score_gemma":0.005753066,"domain_scores_codex":[0.9958743,0.002128153,0.0002527116,0.0007293736,0.0007236993,0.000291644],"domain_scores_gemma":[0.9922058,0.004517122,0.0004466763,0.00179752,0.0008821986,0.00015062],"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":[0.0001025624,0.0000561248,0.0007239483,0.0001842049,0.00006213296,0.0001930283,0.0003500498,0.0210874,0.0008315757,0.8489251,0.01501849,0.1124654],"study_design_scores_gemma":[0.00005013818,0.00005544955,0.000402251,0.000105485,0.00005712466,0.0002367803,0.0001031588,0.1150725,0.001633544,0.8435606,0.03865904,0.0000638699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001578713,0.0008586878,0.9903913,0.0008243218,0.0001376528,0.0000384735,0.0001131332,0.0005957014,0.005462081],"genre_scores_gemma":[0.0877789,0.00182678,0.8869259,0.001349936,0.0006074167,0.0003046306,0.000517485,0.001561744,0.01912721],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01403856,"threshold_uncertainty_score":0.04696369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02534620422468025,"score_gpt":0.2826220769413938,"score_spread":0.2572758727167135,"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."}}