{"id":"W3011188724","doi":"10.1002/cjs.11546","title":"Partial deconvolution estimation in nonparametric regression","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Estimator; Deconvolution; Nonparametric regression; Nonparametric statistics; Kernel (algebra); Mathematics; Regression function; Kernel regression; Convergence (economics); Statistics; Rate of convergence; Covariate; Kernel density estimation; Regression; Applied mathematics; Computer science; Combinatorics","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":[0.01043352,0.0009470418,0.001502068,0.001635383,0.0005092657,0.001299978,0.001695434,0.001386501,0.001193216],"category_scores_gemma":[0.03539883,0.0006471379,0.001163672,0.001539015,0.00288299,0.002150696,0.002481221,0.001658954,0.0003468557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001212841,"about_ca_system_score_gemma":0.002161535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004177947,"about_ca_topic_score_gemma":0.002182514,"domain_scores_codex":[0.9961877,0.002134369,0.0001644724,0.000544339,0.0007731502,0.0001959457],"domain_scores_gemma":[0.986821,0.008380128,0.001150267,0.001674235,0.001778748,0.0001956387],"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":[0.0002427897,0.00005613116,0.00485604,0.000414146,0.0003020407,0.0001555179,0.0002234341,0.3788576,0.005603222,0.4146407,0.001719144,0.1929293],"study_design_scores_gemma":[0.00001379437,0.00003877134,0.001036786,0.00003532198,0.00003584286,0.00006407841,0.00001960834,0.8862485,0.002032251,0.1085722,0.001876065,0.00002676765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007812254,0.0002918722,0.9910511,0.0001224445,0.00001662384,0.00001239317,0.00002336792,0.0001057248,0.000564191],"genre_scores_gemma":[0.5193629,0.001124417,0.4744617,0.0002534968,0.0001459771,0.0001650347,0.0002586219,0.0001578456,0.004070001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01043352,"threshold_uncertainty_score":0.05517834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1192342532065475,"score_gpt":0.3536817071432652,"score_spread":0.2344474539367177,"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."}}