{"id":"W2004086551","doi":"10.1007/s11425-006-2023-3","title":"Hierarchical linear regression models for conditional quantiles","year":2006,"lang":"en","type":"article","venue":"Science in China Series A Mathematics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Outlier; Quantile regression; Covariate; Hierarchical database model; Mathematics; Marginal model; Estimator; Econometrics; Quantile; Multilevel model; Regression analysis; Statistics; Computer science; Data mining","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":[0.01430013,0.001390533,0.002815242,0.00201948,0.001040917,0.003028821,0.006249321,0.002463967,0.01680136],"category_scores_gemma":[0.04480797,0.001529601,0.002679315,0.00370442,0.002562888,0.004762436,0.003280288,0.005055226,0.003116285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002900215,"about_ca_system_score_gemma":0.003309828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02209665,"about_ca_topic_score_gemma":0.02475831,"domain_scores_codex":[0.9917846,0.004976122,0.0002626034,0.001463517,0.0007189562,0.0007942183],"domain_scores_gemma":[0.96027,0.03007163,0.002779166,0.004497243,0.001790451,0.0005914661],"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":[0.0002104944,0.0001222113,0.005940303,0.0002790488,0.0005165533,0.0001974314,0.0005344137,0.2218516,0.0005331365,0.7116153,0.008394813,0.04980481],"study_design_scores_gemma":[0.00005834998,0.00004670781,0.00219786,0.00004664362,0.0001660356,0.00005437043,0.00007266441,0.7014284,0.0001903985,0.2928055,0.002887154,0.00004593748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01438195,0.0007347572,0.9810434,0.0006576843,0.0001022944,0.00006910095,0.0007667109,0.0007307681,0.001513292],"genre_scores_gemma":[0.6351796,0.002536796,0.3194844,0.0008358016,0.0007780323,0.00105598,0.005305927,0.0009574853,0.03386595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02209665,"threshold_uncertainty_score":0.07562721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09705057304052983,"score_gpt":0.427935291405911,"score_spread":0.3308847183653811,"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."}}