{"id":"W4410958474","doi":"10.1080/10485252.2025.2508449","title":"Kernel mode-based varying coefficient models with nonstationary regressors","year":2025,"lang":"en","type":"article","venue":"Journal of nonparametric statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Kernel (algebra); Kernel regression; Statistics; Mode (computer interface); Kernel smoother; Econometrics; Applied mathematics; Kernel method; Nonparametric statistics; Computer science; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000838084,0.0002438267,0.0006030298,0.0008493748,0.0001095109,0.00008191371,0.0003287261,0.00009605671,0.0000675261],"category_scores_gemma":[0.007003703,0.0001806441,0.00008457039,0.001513777,0.0001728723,0.0001056669,0.00003982871,0.0004555254,0.00000325516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001733039,"about_ca_system_score_gemma":0.0006426118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009492257,"about_ca_topic_score_gemma":0.000001039001,"domain_scores_codex":[0.9974203,0.0002112642,0.001006296,0.0002167957,0.0008230552,0.0003223193],"domain_scores_gemma":[0.9863877,0.0111274,0.0007945638,0.0002902249,0.001228601,0.0001715595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005731242,0.0007602733,0.0005645753,0.0003224034,0.0002209829,0.0002601438,0.000196251,0.08926055,0.00003233192,0.8170489,0.006952649,0.08380778],"study_design_scores_gemma":[0.00115954,0.0003821385,0.0002887341,0.0003137676,0.0002167907,0.00003434945,0.0000573839,0.5235007,0.0001544846,0.4736024,0.0001194193,0.00017034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005119887,0.0001619585,0.9905239,0.0001394102,0.0003034346,0.0002024282,0.0002475943,0.00002152893,0.003279872],"genre_scores_gemma":[0.2491592,0.00002771396,0.750386,0.0001885437,0.00002707347,0.000005281788,0.000003988799,0.000020645,0.0001815589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4342401,"threshold_uncertainty_score":0.8384593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07972561807770574,"score_gpt":0.3759112630276199,"score_spread":0.2961856449499142,"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."}}