{"id":"W2065603707","doi":"10.1002/cjs.10031","title":"Local linear fitting and improved estimation near peaks","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Residual; Jump; Mathematics; Statistics; Linear regression; Estimation; Local regression; Least-squares function approximation; Econometrics; Computer science; Applied mathematics; Algorithm; Polynomial regression; Engineering","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":[0.01183566,0.0009232743,0.001930494,0.001929478,0.0004952866,0.001090572,0.002573802,0.00178442,0.002492346],"category_scores_gemma":[0.04730977,0.000796336,0.001279432,0.001914772,0.00136241,0.00187562,0.002672819,0.002219931,0.0009116348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006810319,"about_ca_system_score_gemma":0.000808129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00208795,"about_ca_topic_score_gemma":0.002039892,"domain_scores_codex":[0.9931658,0.00438593,0.0002516953,0.001042378,0.0009117681,0.0002424559],"domain_scores_gemma":[0.9733088,0.01922068,0.001700993,0.003340628,0.00211472,0.000314148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007326088,0.0001937422,0.008759768,0.0004050962,0.0005057885,0.000432164,0.0005255024,0.6400322,0.0132223,0.05702252,0.003198219,0.2749702],"study_design_scores_gemma":[0.00002677761,0.00009783117,0.001557988,0.00002389488,0.00003726101,0.00007930956,0.00002903319,0.9759358,0.003155081,0.01799928,0.001016601,0.00004113647],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01604768,0.0001749773,0.9827768,0.0001069275,0.00001676225,0.00001634138,0.00003200232,0.0004399847,0.0003886523],"genre_scores_gemma":[0.4811335,0.0002282142,0.5144978,0.0002672443,0.0001301683,0.0001348554,0.0003830347,0.000551986,0.002673212],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01183566,"threshold_uncertainty_score":0.0625937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05485012200346461,"score_gpt":0.3579999401714344,"score_spread":0.3031498181679698,"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."}}