{"id":"W2998961846","doi":"10.1214/19-ejs1664","title":"Efficient estimation in expectile regression using envelope models","year":2020,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Estimator; Generalization; Statistics; Conditional probability distribution; Consistency (knowledge bases); Regression; Applied mathematics; Regression analysis; Asymptotic distribution; Econometrics; Mathematical optimization","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":[0.01181891,0.001517165,0.002399395,0.001335526,0.0004172358,0.001610111,0.002491349,0.001834019,0.002781583],"category_scores_gemma":[0.04206573,0.0008897951,0.001825859,0.001576797,0.001367376,0.003937632,0.002892259,0.003077694,0.001314949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006332576,"about_ca_system_score_gemma":0.001050225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002070248,"about_ca_topic_score_gemma":0.001553156,"domain_scores_codex":[0.9944867,0.003670667,0.000200847,0.0007028604,0.0006322297,0.000306647],"domain_scores_gemma":[0.9802126,0.0151354,0.001352067,0.002033622,0.001066614,0.0001996948],"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":[0.0003093326,0.0001376294,0.01010611,0.0003274706,0.0003277008,0.0004233668,0.0003114188,0.6479682,0.002820656,0.1868819,0.003174058,0.1472123],"study_design_scores_gemma":[0.0000121563,0.00004508853,0.001097739,0.00002925872,0.00002355845,0.00007954671,0.00002509169,0.9458532,0.0005972988,0.05115415,0.001056381,0.00002640967],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005568534,0.0001552844,0.9935325,0.0001100009,0.00001225097,0.00001691535,0.00007301973,0.0001797638,0.0003517265],"genre_scores_gemma":[0.5322323,0.001530931,0.4557318,0.0005815149,0.0002544312,0.0004284211,0.001616342,0.0004658868,0.007158375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01181891,"threshold_uncertainty_score":0.06250507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189849950312556,"score_gpt":0.3787226413180383,"score_spread":0.2597376462867828,"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."}}