{"id":"W2740462246","doi":"10.1016/j.jeconom.2017.06.020","title":"Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach","year":2017,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Quantile; Quantile function; Estimator; Nonparametric statistics; Mathematics; Conditional probability distribution; Econometrics; Conditional expectation; Smoothing; Kernel smoother; Cumulative distribution function; Statistics; Kernel method; Computer science; Probability density function; Artificial intelligence","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":[0.005226932,0.0006300503,0.001653917,0.001253735,0.0004722922,0.001564016,0.002777006,0.001317229,0.003606458],"category_scores_gemma":[0.0211332,0.0006872919,0.001174074,0.002036667,0.001074113,0.002620082,0.002560719,0.002075479,0.0007201373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006966445,"about_ca_system_score_gemma":0.001257932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003912572,"about_ca_topic_score_gemma":0.003113975,"domain_scores_codex":[0.997645,0.001435451,0.00009744916,0.0003307786,0.0003636757,0.0001277289],"domain_scores_gemma":[0.9928797,0.0042686,0.0004903291,0.001340325,0.0008775239,0.00014348],"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":[0.0003043925,0.0002117942,0.004664971,0.0002938828,0.0004473086,0.0001956228,0.0002672607,0.4155607,0.00482227,0.2904927,0.004269321,0.2784697],"study_design_scores_gemma":[0.00001225326,0.00002123999,0.0005029572,0.000009293375,0.00003808401,0.00003606616,0.00001427494,0.9621315,0.0004516309,0.03581068,0.0009567444,0.00001527583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003044868,0.00009855189,0.9964079,0.00004895623,0.00001077231,0.00001010297,0.00002084358,0.0001265238,0.0002315063],"genre_scores_gemma":[0.4499485,0.0005776762,0.5434891,0.000141751,0.0001861184,0.0001663246,0.0003836732,0.00038987,0.004716941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005226932,"threshold_uncertainty_score":0.02764297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2717322649653778,"score_gpt":0.3793177546351005,"score_spread":0.1075854896697228,"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."}}