{"id":"W2990181566","doi":"10.3386/t0339","title":"Unconditional Quantile Regressions","year":2007,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canadian Institute for Advanced Research","funders":"","keywords":"Quantile regression; Econometrics; Quantile; Statistics; Cross-sectional regression; Mathematics; Logistic regression; Estimator; Logit; Ordinary least squares; Regression analysis; Nonparametric statistics; Binomial regression; Nonparametric regression; Ordered logit; Censored regression model; Polynomial regression","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006000481,0.0001804022,0.0004371967,0.0006230454,0.0001295944,0.00005053484,0.0005757318,0.0004014638,0.003455776],"category_scores_gemma":[0.00893166,0.0001689533,0.0001551069,0.0001189447,0.0004119051,0.00004681936,0.0006260452,0.001236462,0.0001782956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005097978,"about_ca_system_score_gemma":0.001464605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001680793,"about_ca_topic_score_gemma":0.00003417003,"domain_scores_codex":[0.9969618,0.0003055931,0.0007648918,0.0004732751,0.001126533,0.0003678974],"domain_scores_gemma":[0.9868466,0.01107396,0.0002968568,0.0003804221,0.001254392,0.0001477839],"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":[0.0000308861,0.00009984542,0.0001338477,0.0001442153,0.00005921135,0.000002675044,0.00002995843,0.0000733575,0.00004998158,0.9321097,0.06627154,0.0009947409],"study_design_scores_gemma":[0.0001963273,0.00004305765,0.0006121534,0.0002249382,0.000008210606,0.000003559127,0.0000339947,0.002248036,0.0004819562,0.9951289,0.0008686622,0.0001501478],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02138794,0.0002384385,0.01696295,0.002105345,0.001238016,0.001142916,0.002365042,0.00007051753,0.9544888],"genre_scores_gemma":[0.5986247,0.0001044637,0.394896,0.0000598284,0.001154323,0.0001864597,0.0008638126,0.00006413648,0.004046312],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9504426,"threshold_uncertainty_score":0.9994165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7239146134141636,"score_gpt":0.6561762839538734,"score_spread":0.06773832946029024,"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."}}