{"id":"W3199187583","doi":"10.1002/cjs.11679","title":"Reproducing kernel‐based functional linear expectile regression","year":2021,"lang":"en","type":"article","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 Alberta","funders":"China Postdoctoral Science Foundation; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Reproducing kernel Hilbert space; Functional principal component analysis; Mathematics; Minimax; Estimator; Conditional probability distribution; Kernel principal component analysis; Kernel (algebra); Functional data analysis; Quantile; Kernel method; Mathematical optimization; Applied mathematics; Hilbert space; Computer science; Artificial intelligence; Support vector machine; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.007632651,0.000987194,0.001240343,0.0008577253,0.0003137672,0.001010026,0.002097259,0.001478668,0.003042116],"category_scores_gemma":[0.02204936,0.0004206071,0.001137807,0.0008735062,0.001277176,0.002200632,0.001434737,0.001953645,0.0009695528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006555145,"about_ca_system_score_gemma":0.001073385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001781859,"about_ca_topic_score_gemma":0.001379929,"domain_scores_codex":[0.9974853,0.001695125,0.00007063265,0.0003291015,0.000292714,0.0001270789],"domain_scores_gemma":[0.9924075,0.004919746,0.0007355317,0.0009476189,0.0008562023,0.0001334501],"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":[0.0002821637,0.0001840289,0.006398624,0.0002872748,0.0002558903,0.0002229449,0.0002603491,0.5249302,0.004769271,0.2585179,0.004163458,0.1997279],"study_design_scores_gemma":[0.00000748131,0.00004393498,0.0006397988,0.00000980004,0.00001059367,0.00005100383,0.00001008491,0.9709041,0.0005400282,0.0270192,0.0007462414,0.00001772628],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005170057,0.00007540247,0.9940739,0.0001112656,0.0000108208,0.00002101653,0.00005094513,0.0001548815,0.0003316807],"genre_scores_gemma":[0.6166491,0.0005103244,0.3746083,0.0003456257,0.0001272514,0.0003828636,0.0005914246,0.0003081048,0.006477006],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007632651,"threshold_uncertainty_score":0.04036582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1433939932565675,"score_gpt":0.345506737458725,"score_spread":0.2021127442021574,"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."}}