{"id":"W1934648078","doi":"10.1002/cjs.11215","title":"RKHS‐based functional nonparametric regression for sparse and irregular longitudinal data","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Foundation for the National Institutes of Health; National Science Foundation","keywords":"Reproducing kernel Hilbert space; Nonparametric statistics; Nonparametric regression; Functional data analysis; Functional principal component analysis; Kernel regression; Kernel (algebra); Mathematics; Smoothing; Computer science; Kernel method; Regression; Artificial intelligence; Statistics; Machine learning; Support vector machine; Hilbert space","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001202756,0.0001368147,0.0003158497,0.0002460906,0.0001592708,0.00008165515,0.0002523489,0.00007160391,0.0001741865],"category_scores_gemma":[0.01441622,0.000110937,0.00002929549,0.0001495226,0.0001650534,0.00008043797,0.00002102638,0.0001904081,0.000002084849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005739593,"about_ca_system_score_gemma":0.0006643693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001842772,"about_ca_topic_score_gemma":0.001188914,"domain_scores_codex":[0.9987679,0.0001025535,0.0004565741,0.0001807079,0.0002269536,0.000265365],"domain_scores_gemma":[0.9943355,0.003972202,0.0003179286,0.0003137283,0.0004552187,0.000605477],"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":[0.0001050593,0.00005418129,0.006528646,0.0003215392,0.00007881004,0.00009011121,0.0000604554,0.00003594031,0.00004697221,0.6752861,0.1515392,0.165853],"study_design_scores_gemma":[0.00176367,0.0007457113,0.0398865,0.0003374555,0.0003568968,0.0001845138,0.00007045579,0.1096119,0.00006930638,0.8194861,0.02710765,0.0003797899],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00474204,0.0001274307,0.99272,0.00022924,0.0004420075,0.0001036632,0.001489783,0.00000355729,0.000142258],"genre_scores_gemma":[0.1377352,0.000006841821,0.8617978,0.0001136696,0.0002162043,0.000001764353,0.00004161514,0.0000190125,0.00006790986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1654732,"threshold_uncertainty_score":0.9938858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3225807161975482,"score_gpt":0.3616332932560186,"score_spread":0.03905257705847037,"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."}}