{"id":"W3125130726","doi":"","title":"Predictive Quantile Regression with Mixed Roots and Increasing Dimensions","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lasso (programming language); Quantile regression; Quantile; Econometrics; Oracle; Statistics; Regression; Mathematics; Sample (material); Cross-sectional regression; Regression analysis; Variable (mathematics); Feature selection; Computer science; Artificial intelligence; Polynomial regression; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.007307626,0.001083641,0.00145083,0.0006145961,0.0004367012,0.001577402,0.001793419,0.001143358,0.002562159],"category_scores_gemma":[0.03152203,0.0005303561,0.000719452,0.001312315,0.001464868,0.002164976,0.002265856,0.003471504,0.0004661497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005304693,"about_ca_system_score_gemma":0.0007495018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00162568,"about_ca_topic_score_gemma":0.001291165,"domain_scores_codex":[0.9966359,0.002072936,0.00009774037,0.0004784608,0.0005090715,0.0002059611],"domain_scores_gemma":[0.9768896,0.01879866,0.001549264,0.001530581,0.000897557,0.0003344253],"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":[0.0003809207,0.0001429922,0.01103296,0.0002483592,0.000220659,0.0002847264,0.0001617074,0.7728035,0.002401834,0.1140152,0.002893848,0.09541337],"study_design_scores_gemma":[0.00002151296,0.00003919033,0.0005502705,0.00001309571,0.0000124751,0.00002462804,0.00001393327,0.9678578,0.0004275115,0.03058274,0.0004458478,0.0000110537],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04472357,0.0009141408,0.9516633,0.0007837762,0.0000685546,0.00003208898,0.0001544843,0.000392811,0.00126728],"genre_scores_gemma":[0.8141258,0.001209583,0.1796538,0.0005213207,0.0004121719,0.0001405221,0.0006523159,0.0002092744,0.003075276],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007307626,"threshold_uncertainty_score":0.03864682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1306418424052272,"score_gpt":0.2638571199174573,"score_spread":0.1332152775122301,"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."}}