{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003900261,0.00009274806,0.0002480203,0.0002043667,0.00004819702,0.00003825641,0.0001257274,0.0000623289,0.0002833327],"category_scores_gemma":[0.01472567,0.00008092901,0.00002721292,0.0003515864,0.00006208521,0.00007769715,0.000006281043,0.000255738,0.00001394949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001272564,"about_ca_system_score_gemma":0.0006373208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004083304,"about_ca_topic_score_gemma":0.001137036,"domain_scores_codex":[0.9988648,0.0001174603,0.000545042,0.00008921584,0.0001733078,0.0002102255],"domain_scores_gemma":[0.9979692,0.000916144,0.0002816055,0.00007031355,0.0001918082,0.0005710017],"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":[0.00007183505,0.00004079137,0.01083151,0.0001821544,0.00002833702,0.0007052837,0.001644879,0.0005662963,0.00009496546,0.7623939,0.03392272,0.1895174],"study_design_scores_gemma":[0.001127201,0.0006323367,0.0193169,0.0003065311,0.00008498529,0.00008018065,0.0002335947,0.1921571,0.0003424556,0.7827203,0.002684991,0.0003134632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02105123,0.00008188763,0.9776648,0.0005045519,0.0002268281,0.0000730476,0.0001212757,0.000003785797,0.0002725763],"genre_scores_gemma":[0.476484,0.000007786058,0.5233115,0.0001244655,0.00005365806,6.920133e-7,0.000002400945,0.000007922195,0.000007571583],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4554328,"threshold_uncertainty_score":0.9935737,"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."}}