{"id":"W2612040837","doi":"","title":"On copula-based conditional quantile estimators","year":2017,"lang":"fr","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Group for Research in Decision Analysis; Institut National de la Recherche Scientifique; HEC Montréal","funders":"","keywords":"Quantile; Copula (linguistics); Estimator; Conditional probability distribution; Mathematics; Quantile regression; Econometrics; Covariate; Quantile function; Statistics; Marginal distribution; Asymptotic distribution; Conditional variance; Probability density function; Cumulative distribution function; Random variable; Autoregressive conditional heteroskedasticity","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":[0.01730665,0.001376361,0.002177303,0.003460448,0.0006032785,0.00201973,0.002720003,0.002112108,0.002949239],"category_scores_gemma":[0.07390614,0.0009638467,0.001567764,0.003745497,0.002379211,0.003392676,0.002947586,0.003983233,0.0009068115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145782,"about_ca_system_score_gemma":0.001384325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002813535,"about_ca_topic_score_gemma":0.001619886,"domain_scores_codex":[0.9927512,0.004771254,0.000250217,0.000771862,0.001148857,0.0003066153],"domain_scores_gemma":[0.9679435,0.02537441,0.001575949,0.002311611,0.002486803,0.0003076537],"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":[0.0001144649,0.0001287704,0.00342212,0.0003534823,0.0003243075,0.0002049809,0.0002718696,0.2757525,0.001622418,0.6104996,0.004618367,0.1026871],"study_design_scores_gemma":[0.00003263697,0.00003318838,0.001057684,0.0001087711,0.00004529906,0.00008459292,0.00002520189,0.8216813,0.0009050396,0.1727478,0.003244767,0.00003366414],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002064572,0.0006119572,0.9964851,0.0001190066,0.00003217495,0.00001844634,0.00003192139,0.00009582663,0.0005409874],"genre_scores_gemma":[0.2827328,0.004801913,0.7047204,0.0008764102,0.0008128468,0.0005493407,0.0009061589,0.000669952,0.003930293],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01730665,"threshold_uncertainty_score":0.09152734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1271757796827762,"score_gpt":0.4463058671621519,"score_spread":0.3191300874793758,"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."}}