{"id":"W2769826979","doi":"10.1002/cjs.11511","title":"Optimal estimation in functional linear regression for sparse noise‐contaminated data","year":2019,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Covariance; Mathematics; Minimax; Linear regression; Covariance function; Applied mathematics; Kernel (algebra); Reproducing kernel Hilbert space; Estimation of covariance matrices; Mathematical optimization; Kernel regression; Statistics; Hilbert space","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01262544,0.001239639,0.002190151,0.001657997,0.0005082999,0.001303927,0.002355023,0.00220176,0.001200367],"category_scores_gemma":[0.04131438,0.001084854,0.001486583,0.001530776,0.002408932,0.002288534,0.002416461,0.002386839,0.0003670779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001486677,"about_ca_system_score_gemma":0.002064316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006245321,"about_ca_topic_score_gemma":0.003642306,"domain_scores_codex":[0.9940859,0.004238167,0.0001996601,0.0007665164,0.0005188494,0.0001908829],"domain_scores_gemma":[0.9817061,0.01486482,0.001122942,0.0009577002,0.001144558,0.0002039888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001594996,0.00007860347,0.001767735,0.0002763211,0.0001945761,0.0001317814,0.0001307533,0.8790153,0.002461002,0.06888741,0.00135814,0.04553894],"study_design_scores_gemma":[0.000007962226,0.00001313073,0.0001420691,0.000008395818,0.000005864486,0.00001309303,0.000004992694,0.9884645,0.0002499095,0.01086911,0.0002135771,0.000007343496],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004509018,0.000144706,0.9949037,0.0001895146,0.00001281505,0.00001324695,0.00002619314,0.00007969215,0.0001211263],"genre_scores_gemma":[0.3370774,0.0005898169,0.6586735,0.0003897499,0.0002037244,0.0002981278,0.0005892359,0.0002732282,0.001905284],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01262544,"threshold_uncertainty_score":0.06677049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2844356686254998,"score_gpt":0.3866299702505926,"score_spread":0.1021943016250928,"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."}}