{"id":"W4385702319","doi":"10.1002/cjs.11789","title":"High‐dimensional model averaging for quantile regression","year":2023,"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":"Shandong University; Jinan Science and Technology Bureau; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Overfitting; Quantile; Quantile regression; Computer science; Bayesian probability; Data mining; Regression; Machine learning; Artificial intelligence; Algorithm; Econometrics; Mathematics; Statistics; Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006727853,0.000116864,0.0002852264,0.0002316586,0.0001769651,0.00004271746,0.0001539304,0.0000596733,0.000146489],"category_scores_gemma":[0.004757145,0.0000957411,0.00005399618,0.00015294,0.00007036863,0.00004983153,0.00001146624,0.0001778288,0.00001289498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007964863,"about_ca_system_score_gemma":0.0009096158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002172086,"about_ca_topic_score_gemma":0.0006049166,"domain_scores_codex":[0.9988349,0.00004681706,0.0004613828,0.0001055127,0.0002178319,0.000333525],"domain_scores_gemma":[0.9968487,0.001833458,0.0002427539,0.0001208345,0.000471835,0.0004824298],"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":[0.00001818538,0.000007900481,0.00008930419,0.00007567102,0.00002421935,0.0001366782,0.0002120219,0.001413517,0.0001398482,0.8020471,0.1825505,0.01328498],"study_design_scores_gemma":[0.0003066773,0.00008427429,0.0002622817,0.0001346936,0.00003830916,0.00002507844,0.00004551967,0.2064096,0.0001336778,0.7917295,0.0007216535,0.0001087771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01716803,0.00002693463,0.9801024,0.000344878,0.0006172875,0.00009936869,0.001520452,0.00001162813,0.0001090667],"genre_scores_gemma":[0.1406101,0.000007875503,0.8586714,0.0001260659,0.0001176359,0.000003078513,0.00001693813,0.00002632783,0.0004205567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2049961,"threshold_uncertainty_score":0.569509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1531891241941688,"score_gpt":0.3673239034972121,"score_spread":0.2141347793030433,"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."}}