{"id":"W3028982422","doi":"10.3390/stats3020011","title":"A-Spline Regression for Fitting a Nonparametric Regression Function with Censored Data","year":2020,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Niagara; Brock University","funders":"","keywords":"Smoothing spline; Nonparametric regression; Spline (mechanical); Estimator; Nonparametric statistics; Mathematics; Smoothing; Kernel regression; Kernel smoother; Censored regression model; Regression analysis; Polynomial regression; Censoring (clinical trials); Computer science; Kernel method; Statistics; Artificial intelligence; Spline interpolation; Support vector machine; Engineering; Radial basis function kernel","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003829025,0.0001566507,0.0002699425,0.000046786,0.0001231752,0.00003717105,0.0002315329,0.00006466258,0.00009157535],"category_scores_gemma":[0.006821388,0.00009554537,0.00002596391,0.000361184,0.00003802264,0.0001079835,0.0001371746,0.0001561008,0.00001035165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001481222,"about_ca_system_score_gemma":0.00003982986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004672595,"about_ca_topic_score_gemma":0.00000198816,"domain_scores_codex":[0.9987191,0.00008343619,0.0002788672,0.0004296997,0.0002535018,0.0002353952],"domain_scores_gemma":[0.9971636,0.001888424,0.0001978032,0.000475134,0.0001326874,0.0001423364],"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":[0.003034895,0.0002559362,0.0009058342,0.001294422,0.0001021586,0.00002883083,0.0009825791,0.00001244919,0.004337553,0.08341415,0.07992555,0.8257056],"study_design_scores_gemma":[0.007766679,0.005452435,0.004018264,0.002610319,0.0006851418,0.00002121842,0.001286856,0.4592585,0.009153689,0.4650944,0.043134,0.001518501],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01786305,0.00007023272,0.9797171,0.0007583238,0.000123629,0.0004370903,0.0002938083,0.0001135825,0.0006231287],"genre_scores_gemma":[0.09154338,0.00001240015,0.9076728,0.0002489056,0.0001874305,0.0000220275,0.0001223168,0.00003285061,0.0001578527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8241872,"threshold_uncertainty_score":0.8166331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2806591337634829,"score_gpt":0.433931932130236,"score_spread":0.1532727983667531,"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."}}